Daily, there is much data produced in our digital era. The governments function perfectly with all this data, businesses can grow, and we receive the correct product when we buy things online, such as the correct color of the product.

There's a lot of data and a lot of methods to work with it, but a great deal of things can also go awry. That's why data analysts and engineers use something called data pipelining.

Here, we will discuss what data pipelining is, how it works, what tools are used to implement it, why it is necessary, and how to implement it. Let us first understand what it is and why it is necessary.

Why do we require data pipelines?

Businesses that consume volumes of information require that information to be transported conveniently and quickly from location to location and to be converted into useful information at light speed. Sometimes, however, slow speeds, bad information, or multiple sources giving different information lead to problems.

Data pipelines address these issues by automating the whole process such that all the steps are performed without any intervention. Not all companies require a data pipeline, but it is quite helpful to companies that:

• Develop or utilize a great deal of data from sources

• Require fast or real-time data analysis

• Use cloud storage

• Store data in independent systems.

Data pipelines ensure data to be secure by ensuring that only the appropriate individuals have access to it. Basically, the more data-reliant a business is, the more it requires a data pipeline.

What is a data pipeline?

A data pipeline transfers data from one place to another, just like large pipes transfer water or gas over long distances. It consists of a series of steps, usually performed by specialized programs, that gather, clean, transform, validate, and join data before forwarding it for analysis or use. This makes data transferred rapidly and error-free and without delays.

 Data pipelines for big data manage huge amounts of data that can be well-organized, messy, or partly sorted.

Everything About Data Pipeline Architecture

Data pipeline architecture is the entire system that is meant to collect, organize, and provide data to enable businesses to make informed decisions. It's simply a map that enables data to flow smoothly for easy analysis and reporting.

They employ this system in order to improve business intelligence (BI) and to know things such as how customers behave, automated routines, and user experiences.

Key Components of a Data Pipeline:

• Sources: Where the information is obtained, such as apps, cloud storage, or databases.

• Joins: This operation unites data from various sources following rules.

• Extraction: The action of extracting individual items of information from large sets, such as an area code from a telephone number.

• Standardization: To have information in the same units or form, like measuring miles in kilometers.

• Correction: Error correction in the data, such as misspelled zip codes or ambiguous short forms.

• Loads: Saving cleaned data into the right spot for processing, like a data warehouse.

• Automation: The pipeline operates automatically on a pre-scheduled basis or continuously round the clock, looking for defects and reporting.

Data Pipeline Tools: Made Easy

Data pipelines help move, clean, and organize data to make it available. All the tools in pipelines do three broad things:

1. Collect knowledge from various sources.

2. Clean and change the data to make it useful.

3. Store the data in a single central repository, such as a warehouse or data lake.

There are four common forms of pipeline tools:

1. Group Tools

These tools move large amounts of data at set times, not instantly. Examples include Informatica PowerCenter and IBM InfoSphere DataStage.

2. Cloud-Native Tools

These make use of cloud storage like Amazon Web Services. Companies are saving money because the software is accessed online. Some examples include Blendo and Confluent.

3. Open-Source Tools

These are open-source software written or modified by the companies' own technology groups. Examples include Apache Kafka, Apache Airflow, and Talend.

4. Real-Time Tools

These process data in real-time as and when it comes, such as data from smart sensors or stock exchanges. Some of them are Confluent, Hevo Data, and StreamSets.

Examples of Data Pipelines

• B2B Data Exchange: Companies exchange valuable documents, such as purchase orders or shipping data, between themselves.

• Data Quality Pipeline: This verifies and corrects data, e.g., whether customer names are correct or addresses are correct.

• Master Data Management (MDM): Merges data from various sources and eliminates duplicates to form a single clean, accurate record.

How to Construct a Data Pipeline: Key Things to Observe

Before you create a data pipeline, ask yourself:

• For what purpose is the pipeline? How often will it send data?

• What type of data will it process? How much? Is it clean or dirty?

• What will happen to the data? For reporting, analysis, or automation?

Methods of Building Data Pipelines

• Data Preparation Tools: Simple tools such as spreadsheets display data simply but typically take some manual labor.

• Design Tools: These programs let you put together pipelines using easy drag-and-drop steps.

• Hand Coding: Coding manually with the assistance of tools such as Kafka, SQL, or AWS Glue. It involves programming abilities.

Types of Data Pipeline Designs

• Raw Data Load: Transfers large quantities of data in their raw form.

• Extract-Transform-Load (ETL): Takes data, cleans it, changes it, and saves it in the right place.

• Extract-Load-Transform (ELT): Loads data initially and transforms it subsequently in order to conserve time.

• Data Virtualization: Renders data without copying it, such as in real time.

• Data Stream Processing: Processes data that continuously arrives, processing one event at a time.

How to obtain  Big Data certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Data pipelines are valuable resources that assist in moving and transforming data efficiently and accurately. Choosing the right tools and design makes it easy and efficient to deal with data. A quality data pipeline leads to improved decision-making and business prosperity.

Contact Us For More Information:

Visit :www.icertglobal.com Email : 

Did you ever learn about Big Data before? I am certain that you did. Over the past few decades, everyone has been talking about Big Data. But do you truly know what Big Data is and how it influences our lives and why firms require people who have knowledge of it? In this Big Data Tutorial, I will be discussing everything that you would like to know about Big Data. For more information, you can check out the Big Data Course.

The Tale of Big Data

Earlier, individuals would go from village to village in horse-drawn carts. But as villages grew and became towns, it was difficult to go from one town to another. One individual suggested, "Let's feed the horse more and take proper care of it so that it can work well." It was a good suggestion, but there is a limit to what a horse can do.

Big Data Driver Factors

The amount of data on Earth is growing at a rapid pace due to many reasons. Various sources and our day-to-day life generate a vast volume of data. Ever since the invention of the internet, the whole world has access to it. Whatever we do, we leave behind a digital footprint. With internet-connected smart devices, data is growing even faster.

What is Big Data?

Big Data are extremely large and complicated collections of data. These collections are too large to be stored or processed by ordinary computers or standard procedures. The issues are gathering, structuring, storing, searching, sharing, transporting, analyzing, and presenting this data in a useful format.

Big Data Features

Big Data has five primary characteristics: Volume, Velocity, Variety, Veracity, and Value.

Volume

Volume refers to the amount of data available. The amount of data generated by humans, machines, and social media is huge and increasing every day. As per experts, there will be 40 Zettabytes of data by 2020—that is 300 times the amount of data available in 2005.

Speed

Velocity refers to the rate at which data is generated Many sources create a large amount of data quickly each day. For instance, over 1 billion individuals utilize Facebook on their phones daily. As the usage is growing rapidly, data is being generated rapidly as well. If we can control this pace, then we can make decisions based on real-time data.

Diversity

Variety means the different kinds of information we get. Data are in a structured (organized), semi-structured, or unstructured form. Data used to be primarily from spreadsheets or databases. Today, data are also in the form of images, videos, audio, and sensor data. Because data comes in many forms, it is tough to gather, keep, and work with.

Honesty

Veracity shows if the information is accurate and reliable. Sometimes information is missing or does not appear to be true. For instance, in a table, numbers may be inaccurate or missing. This leads to wrong ideas and makes people doubt the information.

Big data is bewildering and difficult to accept since it is sourced from numerous locations such as Twitter.

Value

Following the reference to volume, velocity, variety, and quality of data, the final important key is value. It does not matter how much data there is if it does not benefit the organization. Data is only useful if it makes the company more money or improved. If data is not valuable, it is not worth anything.

Types of Big Data

Big Data may be classified into three forms:

1.         Organized Information

2.         Semi-Structured Data

3.         Unstructured Data

Structural Information

Structured data is data that is organized in a particular manner, e.g., table format. It is easy to store and manipulate. Data organized in rows and columns is structured data, and special languages like SQL help us work with it.

Semi-Structured Data

Semi-structured information is not in a rigid table format but is instead partly organized. It employs the utilization process of tags and markers to differentiate information so that it is comprehensible to us. XML files and JSON documents are such instances.

Unstructured Data

Unstructured data is not static in nature and is difficult to store in tables. Unstructured data examples consist of text files, photos, video clips, and audio recordings. The volume of such data is expanding rapidly. Estimates suggest that about 80% of data within organizations is unstructured.

How to obtain Big Data  certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Big Data is growing fast and changing the manner in which we use information. Big Data helps organizations to make better-informed decisions through managing large quantities of rapid and diverse data. It is crucial to understand Big Data in order to use the technology today.

Contact Us For More Information:

Visit :www.icertglobal.com Email : 

Everybody knows what a data engineer is, but they are perplexed about what a Big Data Engineer does. It might be even more perplexing to try to figure out what skills are required and how to learn them. But don't worry—you're in the right place! This blog on "Big Data Engineer Skills" will educate you on what a Big Data Engineer does. Then we'll align those tasks to the correct skills and display the best way to learn them.

What is a Data Engineer

A Data Engineer is a person who creates and builds big systems that process lots of data. They ensure that such systems are in proper working order, operate effectively, and are able to process lots of information.

A Data Engineer does what?

Some major duties of Data Engineers are mentioned below:

•Construct and verify large systems that store and respond to data.

• Ensure the systems are strong, quick, and less prone to breaking.

• Control the ETL process—this means that they capture the data, transform it into the appropriate form, and transfer it to where it is needed.

• Customize the system to fit your business needs.

• Enhance the way to gather and utilize data.

• Attempt to make the data more precise and dependable.

• Combine and intermix different tools and programming languages to develop an end-to-end solution.

• Make models to simplify the system and make it less costly.

• Install backup systems in the event of failure.

• Add new tools to enable the system to function optimally.

Big data engineer vs. data engineer: what's the difference?

We are living in a time when information is critical—similar to how gasoline is to automobiles. New methods and tools to utilize information have developed over the years, such as NoSQL databases and Big Data systems.

While Big Data gained popularity, the role of a Data Engineer also evolved. They now have to deal with much more and complicated data. Due to this, they are referred to as Big Data Engineer now. Big Data Engineers must acquire new systems and tools to make, design, and maintain the way big data is gathered and utilized.

What Does a Data Engineer Do?

1. Data Acquisition

This involves gathering data from numerous various sources and storing them in one big reservoir known as a data lake. Data comes in numerous different formats (such as images, videos, or numbers), so Data Engineers need to be aware of how to gather and upload data efficiently.

They employ various methods such as batch loading (loading a lot of data at a time) or real-time loading (loading data as it arrives). They even employ tricks such as loading in stages or loading all at once to expedite the work.

2. Changing Data

Raw data is not necessarily useful at first. It needs to be transformed into a more suitable form. Data Engineers alter the shape or structure of the data as per requirements.

It may be simple or complicated based on the nature of the data. They may employ specialized programs or design their own codes to accomplish this.

3. Performance Optimization

Data Engineers make sure the system is quick and can handle large amounts of data. They make the data flow efficient and allow users to easily utilize reports and dashboards.

They use methods like partitioning (data splitting), indexing (creating a list of data to access quickly), and de-normalization (structuring data to read easily).

Major Responsibilities of a Big Data Engineer

• Establish and maintain data pipelines (data conduits for transfer).

• Capture and convert raw data from diverse sources to facilitate business requirements.

• Speed up the data system by automating operations and rearranging elements.

• Process and store Big Data with Hadoop and NoSQL databases.

• Establish systems to hold and revise data for easy use in reports and analysis.

Skills Required to Work as a Big Data Engineer

• Big Data Tools / Hadoop Frameworks
• Hadoop is an application for processing and storing big data. It was designed by Doug Cutting and is used by numerous organizations today.
• It stores data on several computers and enables engineers to work with data quickly.
• There are many tools in Hadoop, and each tool is a help in several operations in handling big data.
• Big Data Tools You Should Know as a Big Data Engineer

In order to be a Big Data Engineer, you need to learn some special tools. The tools assist you in collecting data, storing data, transmitting data, and processing large volumes of data.

Some of the key ones are listed below:

1. HDFS (Hadoop Distributed File System)

This is where data is kept on many computers. It spreads the data to keep it safe and easy to use. It’s the foundation of Hadoop, so it is important to learn it.

2. YARN

YARN manages resources. It determines how much memory or energy a task requires. It assists in scheduling when jobs must execute.

3. MapReduce

This program helps to handle a vast amount of data by dividing the work into tiny tasks and executing them at the same time. This accelerates work.

4. Hive and Pig

• Hive helps individuals familiar with SQL (computer database language) view data.

Pig is employed in order to transform or mold data using scripts.

They are easy if you know a bit of SQL.

5. Flume and Sqoop

• Flume collects unstructured data, such as logs or text files.

• Sqoop exports and imports structured data (e.g., database tables) from and to Hadoop.

6. ZooKeeper

This utility assists all of the services in the system to coexist. It controls settings and synchronizes them all.

7. Oozie

Oozie is like a planner for tasks. It arranges many small tasks and converts them into a single big task and runs them sequentially.

8. Apache Spark

Spark is used when rapid action is required—such as searching for fraud or making suggestions. It processes data in a fast manner and is Hadoop-compatible.

9. Database Design

Big Data Engineers must be aware of how databases are designed and work. They must be aware of various database designs such as 1-tier, 2-tier, or 3-tier structures and how data is organized.

10. SQL (Structured Query Language)

SQL is used to modify and update the data stored in databases. SQL statements must be familiar to Data Engineers. Knowledge of PL/SQL (an extended form of SQL) is also beneficial.

11. NoSQL (e.g., MongoDB and Cassandra)

When data is not clean columns and rows, NoSQL is employed. NoSQL databases can support a lot of data and enable fast modifications. They are suited for messy or varied types of data.

How to obtain Big Data certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Big Data Engineers work and manage massive data sets using specialized applications and software. It takes mastery of critical skills such as Hadoop, Spark, and SQL to be successful. iCert Global courses will equip you with the appropriate skills for a successful big data career.

Contact Us For More Information:

Visit :www.icertglobal.com Email : 

Hadoop is a robust software program that stores and processes huge amounts of data by splitting it into many computers. This breaks the Big Data into small chunks, making it easier to handle.

HDFS (Hadoop Distributed File System)

HDFS is where data is housed. Imagine it as one large storage building; in reality, however, the data is divided and distributed across numerous computers. The computers work together, with one computer controlling the others. The master is referred to as the NameNode and is the one in charge, with the rest of the computers being referred to as DataNodes.

NameNode

The NameNode is the boss computer. It keeps track of where all the data is saved, the size of the files, and who can access them. If any changes happen, like if a file is deleted, the NameNode notes it down right away. It also checks on the DataNodes regularly to make sure they are working properly.

DataNode

DataNodes are the helper computers. They actually store the real data. When someone wants to read or write data, the DataNodes do the job. They also follow orders from the NameNode to copy, delete, or create data blocks when needed.

YARN helps run and manage tasks in a big data system. It gives the right amount of computer power to make sure everything works well. YARN has two main parts: the ResourceManager and the NodeManager.

ResourceManager

This serves as the central boss for the entire group of computers (a cluster). This operates on the master computer. It is responsible for resource management and determining what has to run.

NodeManager

This component runs on every helper machine (a node). It controls tiny units called containers where jobs execute. It monitors how much computer power it consumes, identifies issues, and keeps records (logs). The NodeManager also communicates with the ResourceManager periodically to remain in touch.

Introduction to Apache Spark

Apache Spark is a powerful tool that can quickly look at and understand data, even while the data is still coming in. It runs on several computers at the same time and uses memory to increase the data processing speed. Since it uses memory instead of slow hard drives, it offers incredible speed. Nonetheless, in order to operate at its best, it needs robust computers.

A unique feature of Spark is called RDD, which is Resilient Distributed Dataset. RDD is a new data structure, a mechanism to store data in Spark. Once an RDD is created, it is immutable, broken into pieces so that different computers in a cluster can work on it independently. RDDs are general-purpose, can hold any kind of data, from numbers and words to even user-defined objects.

Spark Core

Spark Core is the foundational element of Apache Spark, allowing for processing big data on many computers at once. Spark Core also manages computer memory, recovery from failures, scheduling and execution of jobs, and interaction with storage systems.

Spark Streaming

Spark Streaming allows Spark to handle data as it comes in real time, such as a video feed or sensor alerts. It handles large amounts of live data in an efficient and reliable way.

Spark SQL

Spark SQL enables the use of SQL, a data manipulation language, in the Spark environment. If you are already familiar with using some tools such as MySQL or some other database, you are familiar with Spark SQL. It helps you to use tables and execute queries in a very simple manner.

GraphX

GraphX is a Spark module that enables you to deal with graphs. A graph consists of points, or nodes, and lines, or edges, that join them together. A map or social network would be an example of a graph. GraphX enables you to examine such networks with Spark.

MLlib (Machine Learning Library)

MLlib is Spark's machine learning tool that allows you to develop smart programs that can learn from data and predict future occurrences. It can be used to perform a wide variety of tasks, including finding patterns or predicting future trends.

Spark works well with many languages like Python, Java, Scala, R, and SQL. Additionally, it works perfectly with other tools, allowing you to create sturdy data projects that take advantage of its many features like MLlib, GraphX, SQL, and Streaming.

A Simple Comparison between Hadoop and Apache Spark

1. Performance and Speed

Spark beats Hadoop by holding most of its data in memory (RAM) while processing. Even when data is so much that it becomes overwhelming, it still has the ability to make use of disk space. Spark is most suitable for operations that require immediate results, like credit card checks, machine learning, security checks, and the operation of smart devices like sensors.

Hadoop collects enormous data from various sources and scatters them across many computers. Hadoop uses a mechanism known as MapReduce to process the data in batches, going through chunks of data over a period of time. Because of this, it is slower than Spark and is not as appropriate for real-time use.

2. Ease of Use

Spark is simple to use and works with many languages like Java, Python, Scala, and SQL. It also provides developers with the ability to test and see results immediately with an interactive shell, making it easier to develop with Spark.

Hadoop can easily get data from tools like Sqoop and Flume. It can also be readily integrated with other software such as Hive and Pig. For SQL-savvy individuals, Hive is a blessing because it lets them use big data with the familiar commands.

3. Cost

Both Hadoop and Spark are open-source. They are free software. The main cost is in terms of computers and servers required to run them.

• Hadoop holds data on disk, thereby requiring additional room and additional machines to read and write data in a timely fashion.

• Spark employs memory (RAM), which is more expensive, but it requires less machine since it is quicker. Hence, in the long run, it can prove to be cheaper, particularly when less system is required to accomplish the task.

4. Data Processing

There are two main types of data processing:

• Batch Processing – It is collecting massive data initially and processing it afterwards. It is suitable for analyzing something that already occurred in the past. Example: Finding the average income of a nation over the past 10 years.

• Stream Processing – This involves processing data when it comes in. It is helpful if updates are required quickly or decisions have to be made rapidly. Example: Fraud detection in a credit card transaction.

How to obtain certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Apache Spark is faster and better for real-time data, while Hadoop works well for large batch processing. Spark is easier to use and supports more features. Both are important tools for handling big data, depending on your needs.

Contact Us For More Information:

Visit :www.icertglobal.com Email : 

Why Big Data Analytics Is a Smart Career Choice

Big Data surrounds us. There is a huge volume of information generated every second from applications, websites, and devices. People want to save and store it because it may be useful. Saving the data is not sufficient — we must know how to utilize it.

That is where Big Data Analytics comes in. It enables businesses to improve decision-making, run their businesses more effectively, and remain competitive. That is why Big Data Analytics is today one of the most critical components of the tech sector.

Big Data = Big Career Growth

If you are still wondering why it would be a great idea to learn Big Data, here is the number one reason:

1. Enormous Demand for Data Specialists

There are more Big Data positions open today than ever. Jeanne Harris, a leading business guru, once said, "Data is useless without the skill to analyze it." What this means is that possessing data is not enough — we need people who can read and apply it.

2. Plenty of Jobs, But Not Enough Trained Individuals

Most companies are ready to employ data-working individuals, and they are growing in number at a rapid rate. But there is a catch here — there are not enough individuals with the ability to perform such tasks. All this is taking place everywhere, not in any specific nation.

3. Big Data careers are highly lucrative.

Big Data career is rewarding because most organizations require data-savvy professionals, and therefore the compensation for Big Data is on the rise. Individuals with the required skills are prospering everywhere globally — not only in some country.

In India, employees of Analytics get higher pay compared to the majority of other IT professionals. A study revealed that the compensation of data professionals in India grew by 21% in a span of one year. It also stated that 14% of them receive over ₹15 lakh annually, which is a highly satisfactory salary.

4. Big Data Analytics is Extremely Critical to Businesses

A survey titled 'Peer Research – Big Data Analytics' revealed that most businesses now rate Big Data Analytics as one of their top priorities.

Why? Because they feel it makes their business stronger. With information, companies can operate more effectively, make better choices, and achieve their goals sooner.

5. Increasingly More People Are Adopting Big Data Analysis

New technology and tools are assisting us to interpret enormous and diverse quantities of data. Because of this, more companies are applying Big Data Analytics to make sound decisions.

6. Analytics Enables Firms to Make Informed Decisions

Analytics is of crucial significance to most companies. In a survey titled 'Analytics Advantage', almost 96% of the participants said that analytics would be of even more importance to their companies in the next three years. Nowadays, there is a great deal of data that is not being leveraged by companies. Most of the analytics that are currently being done are extremely simple.

7. Increasingly, Companies are Utilizing Fresh Kinds of Data

A study titled 'Peer Research – Big Data Analytics' states that the majority of companies these days are employing various forms of data that are not structured like normal numbers or tables. Approximately 84% of the respondents indicated that their companies are analyzing and studying web logs (website visit histories), social media updates, e-mails, images, and videos.

8. Big Data Analytics is applied in numerous diverse industries.

Big Data Analytics is so well-liked because it assists in so many various ways. It is utilized by so many various kinds of business and industry on a daily basis. This is the reason that Big Data professionals are now so highly sought so rapidly — because it can assist in enhancing nearly any kind of work.

9. Big Data Analytics Is Growing Faster Than We Expected

Big Data Analytics is a technology that isprogressing very rapidly and would revolutionize most industries very soon. The Big Data Analytics market will be valued at $125 billion globally, according to experts. Big Data software will ensure computers and data are kept secure by identifying and defeating security threats using intelligent approaches such as machine learning.

10. Abundant Career Opportunities in Big Data Analytics

Big Data Analytics has numerous types of employment opportunities to be filled. Since numerous industries utilize data, there are numerous types of titles and roles, including:

• Big Data Business Consultant

• Big Data Architect

• Big Data Engineer

You may also work in other analytical fields like:

• Prescriptive Analytics (telling what to do next)

• Predictive Analytics (guessing what might happen)

• Descriptive Analytics (telling what occurred)

How to obtain certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Big Data Analytics is a rapidly growing industry with ample job opportunities since businesses require professionals to comprehend and make use of data. There are not enough skilled professionals at present, and acquiring such skills can result in a good job. It's a good option for anyone who likes working with technology and resolving problems.

Contact Us For More Information:

Visit : www.icertglobal.com     Email : info@icertglobal.com

Big Data is everywhere. Businesses would like to capture and store as much data as they possibly can so they will not be missing out on anything significant. There are a lot of data generated on a daily basis, and how we make use of that data matters. That is why Big Data Analytics matters nowadays in the tech industry. It enables businesses to make informed decisions, enhance their operations, and gain a competitive advantage over other businesses. It is beneficial to businesses and data professionals.

1. Many need data specialists.

Jeanne Harris, a prominent leader in Accenture, also said that data is meaningless if no one is there to analyze it. There are increasing Big Data and Analytics career opportunities each year. That is why most tech personnel are spending money and time acquiring these skills.

Big Data Analytics is growing very fast.

The need for data professionals is just starting. Srikanth Velamakanni, CEO, and co-founder of Fractal Analytics believes that within a couple of years, the analytics business will account for a third of the IT industry in the world. It accounts for a tenth now.

2. Lots of Work, Yet Not Enough Qualified Individuals

The demand for Big Data Analytics roles is rising exponentially, but there are not enough trained professionals to fill them. This is true worldwide, not in any specific region. Despite Big Data Analytics being a desirable and rewarding career, many organizations still have vacancies since they are unable to find the perfect candidates.

3. Big Pay in Big Data Analysis

There is a vast demand for Big Data professionals, and thus businesses are offering high pay to recruit the right talent. This is being done globally, in nations such as Australia and the U.K., where pay for data professions is increasing day by day.

4. Big Data Analytics is a Top Priority for Businesses

A survey known as 'Peer Research – Big Data Analytics' discovered that most firms believe Big Data is extremely important. Such firms are convinced that the application of Big Data assists them in performing their work more effectively and enhancing their outcomes.

5. More and more companies use big data analytics.

New technology facilitates the analysis of large and diverse sets of data. In a TDWI report, over one-third of the individuals who were surveyed are already implementing Big Data for applications such as Business Intelligence, Predictive Analytics, and Data Mining.

6. Analytics Enables Companies to Make Smarter Decisions

All businesses now see analytics as a critical way to keep pace. In a 'Analytics Advantage' survey by Tom Davenport, 96% of individuals believe analytics will be more important in the next three years. A lot of data now sit idle, and simple analysis only is being performed.

7. More and more businesses are leveraging new data sources.

The 'Peer Research – Big Data Analytics' poll indicates that firms are utilizing more unstructured and semistructured data more heavily. This implies that they are examining such things as social media updates, emails, images, videos, and web logs.

8. Big Data Analytics is Applied Across Multiple Disciplines

Big Data Analytics is in vogue due to its positive attributes and the way it assists in numerous ways. One of the key factors it's developing at a very rapid rate is that it's being implemented in numerous industries, such as healthcare, banking, marketing, and so on. This increases the demand for Big Data skills even further.

9. Big Data Analytics is Growing Faster Than Expected

Big Data Analytics is turning out to be one of the most powerful technologies of the future. In a survey by Nimbus Ninety, it is forecasted to have the biggest impact between the next three years.

10. Plentiful Career Options in Big Data Analytics

If you wish to pursue a Big Data Analytics career, you have numerous choices! Since it is employed across so many fields, you can select from numerous various job titles and job categories.

Some occupational names include:

• Big Data Business Consultant
• Big Data Engineer

How to obtain  Big Data certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Big Data Analytics is a wise career decision with numerous benefits. It is expanding rapidly, applied in numerous industries, and provides lucrative jobs. There are numerous career options to pick from and great demand worldwide, so let's begin learning. With training programs such as those provided by iCert Global, you can enhance your skills and set yourself up for a brighter future in Big Data Analytics.

Contact Us For More Information:

Visit : www.icertglobal.com     Email : info@icertglobal.com

Before we address the problems of Big Data, let us know what Big Data is. "Data" is any information that a computer can utilize. Nevertheless, such information is of little use unless we structure it and maintain it.

The 5 V's of Big Data :

Big Data is generally defined by five V's:

1. Volume – A massive volume of data is being generated

2. Velocity – Data is created and shared quickly.

3. Variety – There are numerous varieties of data (words, pictures, video, etc.)

4. Value – Data is helpful when it provides useful information.

5. Truthfulness – Information should be true and reliable

Big Data Case Study: Google

When more people began accessing the Internet, Google began having a hard time keeping all of the search data on its normal computers. With thousands of searches being made per second, it needed a better way to process it.

This system comprises:

• A single master computer that knows where the information is.

• Multiple chunk servers, or helpers, where the actual data is stored.

• If there is a need for data, the master informs the user where to find it, and then the helper servers retrieve the data.

Challenges of Big Data

Big Data is very helpful, but it also has some problems. Let us talk about them:

1. Storage Problems

Each day, huge amounts of data are generated in the text, image, and video format. Such unstructured data cannot be stored by normal systems, so we require special programs.

2. Processing Problems

Before we are able to use data, we have to read it, clean it, and structure it. This is referred to as processing. However, as Big Data is so massive and in so many various forms, this can be very time-consuming and labor-intensive.

3. Security Risks

Data needs to be guarded. If it is not encrypted or locked, hackers will steal or erase it. As a result, firms need to develop a system to guard data but enable access to the right individuals.

4. Enhancing Data Quality

Sometimes there is poor data. Below are four ways to correct poor data:

• Identify and rectify the errors in the original source of the data.

• Clean the raw data source.

• Use smart ways to confirm the individual's identity.

• Employ software that helps in cleaning and organizing vast amounts of data.

5. Scaling Big Data

As the data gets larger, companies use intelligent methods of managing the data more effectively, such as:

• Splitting data into smaller pieces

• Using cloud storage

• Separating data that is read-only and data that can be changed

6. Choosing the Right Tools

There are a number of tools to employ with Big Data. Some of the most popular are:

• Hadoop

• Apache Spark

• NoSQL Databases

• R Programming

• Predictive Analytics (to guess future trends)

• Prescriptive Analytics (to make recommendations)

7. Big Data Environments

Big Data comes from all kinds of various sources at all times. Because of this, it's hard to remember where each part came from or what it's used for. That's why it is hard to manage this setup.

8. Current Information

Real-time analytics is processing data at the moment when it arrives. This assists in taking swift and intelligent decisions based on figures and logic.

9. Data Validation

Before we can utilize data, we need to ensure that it is correct and in the right format. This process of checking data is called data validation. It ensures data is useful to utilize in analysis, reports, or even machine learning.

Security Concerns in Big Data Sets

Big Data Security refers to guarding all that information against malicious individuals or attacks. Big Data is harmed by such as:

• Hackers taking information

• Denial-of-service attacks (when systems become overloaded)

• Ransomware (when somebody encrypts your files and asks for money)

Challenges with Cloud Security

Cloud Security Governance is the practice of following some rules to protect cloud information. Cloud information is information that is kept on the internet rather than on your device.

Some typical problems are:

• Ensuring how effectively the cloud system operates

• Guarantee that rules are followed

• Controlling the funds required to run such systems

How do we fix these problems?

Let's consider a software that assists us in working with Big Data:

Hadoop: A Tool for Big Data

Hadoop is an open-source software that allows storing and processing Big Data on inexpensive and basic computers. It consists of two primary components:

1. HDFS (Hadoop Distributed File System)

• This is where data are stored.
• it's safe, can get larger, and still functions even when some computers fail.
• Starting from version 2, it stores data in blocks of 128 MB or larger.
• It is able to work on numerous computers simultaneously.
• Hadoop makes it easy and less expensive for companies to store and protect Big Data.

2. Hadoop Ecosystem (Simplified Explanation)

Hadoop is a computer program that helps store, move, and secure Big Data. It secures data by encryption whether it's stored or it's in transit between two computers. Hadoop utilizes multiple computers that work together within a cluster of computers.

These are the fundamental components of the Hadoop system:

• Sqoop

Aids in the migration of structured data from ordinary databases to Hadoop, and vice versa if necessary.

• Flume

Imports fast or dirty data (e.g., logs or social media) into Hadoop or a tool like Hive.

• Hive

A data warehouse is a tool. You can use SQL, which is an easy language, to ask questions and find useful information.

• HCatalog

Allows users to save data in various forms and formats.

• Oozie

A scheduler used for executing jobs at the right time in the Hadoop ecosystem.

What is MapReduce?

MapReduce is an old but powerful method that Hadoop uses to handle Big Data. It splits tasks into two easy steps:

1. Map Step

• It examines all pieces of information.
• Organizes it.
• Determines how much to accomplish at once.

2. Reduction Phase

• Organizes related data.
• Eliminates wrong or redundant information.
• Keeps only the essential pieces.

How to obtain Big Data  certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Big Data can be tricky to handle, but the likes of Hadoop and its convenient system make it easier to handle and understand data. Big Data software allows us to store data, protect it, and discover useful insights. If you want to learn more and boost your career in this field, look at the Big Data and Hadoop training courses by iCert Global. They make learning easier and allow you to prepare for a job.

Contact Us For More Information:

Visit : www.icertglobal.com     Email : info@icertglobal.com

Raw data is of no use by itself. Data processing transforms raw data into comprehensible information. It involves gathering, cleaning, sorting, processing, and analyzing data before presenting it in a format easy to comprehend.

Six Steps of Data Processing

The cycle of data processing contains six significant steps:

1. Collection: Raw data is collected from various sources. Data can be numbers, user behavior, or even company accounts. The quality of data collected will decide the quality of the end product.

2. Preparation: Data preparation entails cleaning it to remove errors, duplicates, or blanks. The objective is to obtain quality data that is further analyzable.

3. The processed data : is converted into a format suitable for machine interpretation and entered into the system. It can be entered manually, scanned, or entered from external sources like APIs or databases.

4. Raw information: is transformed with the help of algorithms (such as AI or machine learning) to produce meaningful outcomes. Data processing can vary based on data nature and its purpose.

5. Output: The processed information is presented in an organized manner, such as charts, tables, or documents. The output is stored and can be used for further analysis.

6. Storage: Lastly, the data is stored for easy retrieval in the future. This helps improve user experience and smoothen the process in the next cycle.

Methods for Data Processing

There are various ways of processing information depending on the source of information and what has to be done to it. There are five broad categories as follows:

• Batch Processing: Data is collected over a set period and processed in bulk afterward. It is used in situations where time is not critical, e.g., payroll systems.

• Real-time processing: The data is processed as soon as it is entered, most appropriately used in applications that must respond rapidly, i.e., ATM withdrawals.

• Online Processing: Information is constantly entered into the system and is processed immediately. It is normally applied to operations such as scanning bar codes at the checkout counter.

• Multiprocessing: Data processing is carried out by multiple CPUs simultaneously, appropriate for operations such as weather forecasting that involve extensive processing.

• Time-sharing: The computer facilities are shared with numerous users by dividing time into tiny portions so that numerous users are able to use the system simultaneously.

Data Processing Procedures

There are several ways to handle data, including:

1. Manual Data Processing: Human beings do everything manually, without the assistance of tools or machines. It is cheap but can lead to errors and is not effective.

2. Mechanical Data Processing: Calculators and typewriters are tools facilitating data processing. Neither create more errors than if done manually nor do they have any when dealing with sets of data.

3. Electronic Data Processing: Computer technology processes the data, e.g., data processing software. It is accurate and fast but expensive.

4. Distributed Processing: The information is processed on more than one computer, hence it is quicker and more trustworthy. It assists in processing large tasks.

5. Automatic Data Processing: Software executes repetitive tasks automatically, eliminating human mistakes and boosting efficiency.

Shared Data Processing Tools

Among the shared instruments utilized to control data are:

• Apache Hadoop: Open-source framework for handling big data in numerous computers using MapReduce.

• Apache Spark: Spark performs data processing in memory, supporting both batch and streaming data.

• Google BigQuery: Cloud-based solution for fast analysis of large data, scalable to address increasing business data needs.

• Talend: A user-friendly software for information processing and management from various sources, convenient for businesses handling lots of data.

Data processing is one of the most significant activities of data science in the present times. It transforms raw data into meaningful information that aids business growth. With proper tools and procedures, businesses can find valuable insights and make informed decisions.

Microsoft Azure Data Factory

Microsoft Azure Data Factory is a cloud service that enables organizations to design, operate, and manage data pipelines. It enables businesses to process data in batch mode and stream mode, which is useful for different data processing requirements.

Major benefits of Azure Data Factory are:

• Cloud architecture: No equipment or infrastructure on-premises.

• User-friendliness: Drag-and-drop interface to create data pipelines.

• Batch and streaming support: Support data in both modes.

• Integration: Seamless integration with other Azure services.

• Scalability: Scalability in order to address the business needs.

It is a market-leading solution for organizations that need to handle huge amounts of data and process data automatically in the cloud.

Methods of Handling Data

Data processing is ubiquitous and pervades most industries and our everyday lives. Some of the examples of data processing in everyday life are given below:

1. Stock Trading Platforms:

These sites have access to current market data and process thousands of transactions every second

2. E-commerce Personalization:

Online shops collect and analyze customers' actions, such as visit history and past buys.

3. ride-hailing apps:

Ride-hailing services such as Uber and Lyft process real-time location and traffic data to enhance user experience.

How to obtain Big Data certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Final thoughts

Data processing helps in converting raw data into useful information for customers and businesses. Data processing makes decisions easy and faster. Learning data processing is a smart career choice in the IT industry. iCert Global offers great courses that can help you learn these skills easily.

Contact Us For More Information:

Visit : www.icertglobal.com     Email : info@icertglobal.com

A tremendous amount of data is generated every day through activities like online shopping, social media, and payments. Statista states that by 2025, the world will have generated 175 zettabytes of data. With more and more people accessing the internet, it is extremely important to understand and handle this data. Data processing assists in converting raw data into useful information.

What is data processing?

Raw data is of no value to any company. Data processing is when we process raw data and make it valuable. Data scientists and engineers usually do it in phases. They obtain the raw data first, clean and organize it, sort it, process it, analyze it, store it, and present it in an understandable form.

Data processing is important. It helps businesses make better decisions. It also helps them stay ahead of the competition. By transforming data into usable formats like charts, graphs, and reports, employees can better understand and use the information.

There are typically six principal steps in the data processing cycle:

Step 1: Collection

The initial step in data processing is obtaining raw data. The type of raw data obtained matters as it affects the outcome. Obtaining data from reliable sources is required to provide results that are credible.

Raw data may have:

• Turnover statistics
• Website data
• Company profit/loss statements
• User behavior

Step 2: Preparation Data preparation, or cleaning, is sorting and classifying the raw data to eliminate unwanted or erroneous information. It is looking for errors, duplicates, or missing information.

Step 3: Input Here, the cleaned and prepared data is converted into a computer-readable format and fed into the system. Data may be entered manually, scanned from paper documents, or even uploaded from digital sources such as APIs or databases

Step 4: Data Processing

We employ a number of ways and methods by means of implementation through this step. Machine learning and AI assist us with processing the data and obtaining desired results.

Step 5: Output After processing the data, it is displayed to the user in a easy-to-grasp manner, like graphs, tables, reports, or videos. The output can be saved and used in the next data processing cycle.

Step 6: Storage The last step is to save the metadata and data for future reference. This is convenient for retrieving information later, and it can also be utilized in the subsequent data processing cycle.

Data Processing Methods

There are five separate methods of processing data: manual, mechanical, electronic, distributed, and automatic. Let's discuss them all a little further:

1. Manual Data Processing:  In manual data processing, everything is done by hand. People gather, filter, sort, and calculate data without the use of machines or software.

2. Mechanical Data Processing : Mechanical data processing utilizes tools or equipment like calculators, typewriters, and printing presses. It is quicker and less error-prone than manual work. However, it can still be tedious with gigantic data.

3. Electronic Data Processing :  Through this, the most recent technology like computers and data processing software is used to process data.

4. Distributed Processing : Distributed processing refers to dividing the job among multiple devices or computers. This is faster and more reliable since there are multiple systems involved.

5. Automatic Data Processing : Automatic data processing employs software to carry out procedures automatically. It accelerates, reduces errors, and enables individuals to focus on significant tasks rather than repeating the same thing repeatedly.

Data Processing Tools

Some of the most commonly used tools that organizations use in order to handle, process, and analyze massive amounts of data are as follows:

1. Apache Hadoop : is a computer program that helps in storing and handling much information on a very large number of computers. Hadoop can work with big data and do so effectively.

2. Apache Spark : Apache Spark is another fast and free tool. It is faster than most other tools since it works on data in memory and not on the disk.

3. Google BigQuery : Google BigQuery is a web-based tool through which users analyze big datasets quite quickly. Huge data can be processed quickly with it, that is, within seconds. Google BigQuery gets along well with other Google Cloud services too.

4. Talend : Talend is a business solution that makes it possible to connect and work with data from any place. Talend simplifies data cleansing, processing, and data movement.

5. Microsoft Azure : Data Factory Microsoft Azure Data Factory is a cloud-based service to assist businesses in designing and orchestrating data pipelines. Both real-time data and batch data can be processed by it, and it is well-suited for use with other Microsoft Azure products.

Examples of Data Processing

Data processing is taking place everywhere in the world, but we are not even aware. Some of these are actual applications of data processing:

1. Stock Trading Platforms Stock trading platforms process real-time market information, verifying numerous transactions per second.

2. Personalization in E-commerce Online stores study customer browsing and buying history to suggest products. This improves the shopping experience and boosts sales.

3. Ride-Hailing Apps Ride-hailing  apps such as Uber use geolocation and real-time traffic information. They utilize the data to determine best routes, apply dynamic pricing, and dispatch drivers to riders efficiently and quickly.

How to obtain Big Data certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Data is increasing at a very fast pace, and the demand for professionals to process and manage it is increasing. Data processing is becoming faster and efficient with the development of technology such as cloud computing. iCert Global's Professional Certificate Program in Data Engineering provides you with hands-on training to achieve your goals in data engineering. It is a wonderful chance to build your career in data engineering.

Contact Us For More Information:

Visit : www.icertglobal.com     Email : info@icertglobal.com

Hadoop enables businesses to utilize plenty of data in order to make sound decisions and come up with new ideas. Currently, businesses produce more data than before, and therefore, businesses need individuals who can work with Hadoop.

What is Hadoop?

Hadoop is open-source software that enables the storage and processing of big data across numerous computers. It employs a paradigm known as MapReduce, which facilitates the processing of big data by breaking it into small tasks.

Hadoop has four major components:

• Hadoop Distributed File System (HDFS): It stores the data. It breaks large files into small fragments and stores them on numerous computers. Each fragment is duplicated numerous times so that the data is preserved, even if a computer crashes.

• MapReduce: That's how Hadoop handles data. First, it sorts the data in the "Map" process. Then the "Reduce" process adds up or pulls useful information out. It is rapid because it exploits many computers at the same time.

• Hadoop YARN (Yet Another Resource Negotiator): YARN is used to manage the computers in the Hadoop system. YARN makes sure that all tasks are given the appropriate amount of computer power and memory. It also enables multiple types of data to exist side by side—such as live data and stored data.

• Hadoop Common: These are the libraries and utilities that assist the other components of Hadoop. It gives the overall support and combines all the components together.

Benefits of Using Hadoop

Hadoop is an effective way to manage big data. The most significant reasons why individuals utilize it are:

• Grows with Your Data (Scalability): It is easy to add more computers to a Hadoop cluster when your data expands.

• Saves Money (Cost-Effective): Hadoop operates on basic, low-cost machines. You don't require fancy, high-end computers to implement it. Moreover, as it is free to use (open-source), you don't need to pay for a license.

• Flexible (Compatible with All Types of Data): Hadoop can process all types of data—words, images, videos, etc.

• Preserves Data Intact (Fault Tolerant): Hadoop creates duplicate copies of your data and stores them elsewhere. Therefore, even if a computer crashes, your data is still intact and the system keeps running.

• Quick and Efficient: Hadoop divides large jobs into small ones and runs them at the same time using a large number of computers.

• Holds a lot of raw data (Data Lakes): Hadoop is able to gather and store a lot of data in one location, even though you may not yet know how you will use it.

• Well-established Support System (Ecosystem)

Top Hadoop Skills

As big data keeps growing, Hadoop has become a vital resource for individuals who want to deal with large amounts of data. To understand Hadoop is to acquire a range of vital skills beyond the processing of simple data. The following are the most vital skills one needs to become a Hadoop and big data expert:

1. Hadoop Basics Understanding

Hadoop is free software that helps divide big tasks among many computers to make them faster. It keeps big files by dividing them into blocks and placing them on different computers. It then sends small pieces of code to all the computers so that they can work on their part of the data at the same time.

The two main constituents of Hadoop are:

• Hadoop Distributed File System (HDFS): It stores your data on numerous computers.

• MapReduce: This section assists in processing the data by dividing the task into pieces and running them simultaneously on other computers.

2. Hadoop Distributed File System (HDFS)

HDFS is Hadoop's storage mechanism. HDFS is designed to be robust and stable even in the case of low-cost computers. HDFS is suitable for applications dealing with extremely large data volumes.

3. Data Loading Tools

To utilize data in Hadoop, you must first load it into the system. That is where you need data loading tools. They assist you in loading data into HDFS or tools such as Hive and HBase.

Two popular tools are:

• Sqoop: Imports bulk data from regular databases into Hadoop.

• Flume: Gathers and shoves log information (e.g., from sites or servers) into HDFS.

4. HiveQL

HiveQL is a proprietary language that is utilized to query (termed as queries) in Apache Hive, a utility to facilitate working with data stored in Hadoop. HiveQL is very similar to SQL, the language utilized in conventional databases.

Although Hive runs on Hadoop, you do not need to write complex code. You can simply write simple HiveQL queries, and Hive will convert them into MapReduce jobs in the background. HiveQL also supports more complex data types such as lists and objects so that it can handle dirty or big data sets.

5. Apache HBase

HBase is a special kind of database that is HDFS-based. It is used to handle extremely large tables with millions of columns and billions of rows. HBase keeps data in columns rather than rows and is extremely simple to scale by just adding more machines.

Significance of Hadoop Skills

Information is growing faster today than ever before. Companies require smart means of storing, processing, and interpreting the data. Hadoop contributes to giving a strong foundation to big data. That is why it is essential to learn Hadoop:

• Working with Big Data: Hadoop is able to work with lots of data on many computers. Companies with petabytes of data find this useful. People who know Hadoop help with these large collections of data.

• Cost-Effective Expansion: With Hadoop, businesses are able to expand their data storage and processing capacity without breaking the bank. Individuals who understand Hadoop can help businesses expand their systems without exceeding their budget.

• Processing Any Type of Data: Hadoop can process any form of data, whether structured (such as numbers) or unstructured (such as images or text). This enables companies to comprehend and leverage various forms of data in a bid to make informed decisions.

• Data Security: The more companies are getting hacked, the more critical it is to safeguard data. Hadoop's security is good, and those who are able to use it can secure data and keep it confidential.

• Leveraging New Ideas: Hadoop also has other powerful tools (such as Apache Spark, Hive, and Pig) that enable individuals to analyze data in improved manners, even immediately. This might guide companies to make good choices and explore new business plans.

Career Development Prospects with Hadoop

Learning Hadoop and big data skills can open many great career doors. Because businesses create more data, they need people who can handle big, complicated datasets and deliver valuable information. That is how having Hadoop in your arsenal can propel your career:

1. Data Scientist

Hadoop professionals are highly sought-after data scientists. They use Hadoop to work with huge sets of data and use statistical models to find patterns, make predictions, and deliver meaningful information. Such a role generally requires proficiency in machine learning and data mining.

2. Big Data Engineer

Big data engineers create and manage data systems that handle big data, like Hadoop. They enable the unrestricted flow of data between systems so that businesses can analyze data efficiently.

3. Data Analyst

Data analysts use Hadoop to navigate through data and generate reports, visualizations, and business intelligence. They typically employ tools such as Hive or Pig to pose questions to large datasets and provide insightful data analysis.

4. Machine Learning Engineer

Big data, processed using Hadoop, is employed by machine learning engineers to train models and build data-driven decision systems. Huge quantities of data are processed and stored by Hadoop, making it a very valuable tool for machine learning practitioners.

5. Hadoop Developer

Hadoop developers develop applications to run data using Hadoop. They should be able to code in Java, Python, or Scala and understand the key parts of Hadoop.

Improving Your Career

To enhance your career in Hadoop, it's also necessary to pay attention to other aspects:

• Soft Skills: Communication, leadership, and project management skills are extremely important when you assume senior positions.

• Certifications: Hadoop and associated technologies certifications are helpful in proving your expertise and standing out in the job market.

How to obtain  Hadoop certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

To be well-versed in Hadoop, one needs a number of skills like programming, data analysis, problem-solving, and communication. By learning these skills, one can fully utilize all the strengths of Hadoop's system and become highly valuable in the fast-growing field of big data analysis.

iCert Global provides a great Post Graduate Program in Data Engineering for people who want to start this valuable learning experience. This detailed course includes everything about Hadoop, like HDFS, MapReduce, YARN, Hive, Pig, Spark, and more. With practical projects, real-life examples, and expert help, learners get hands-on experience and build confidence in using Hadoop for big data solutions.

Contact Us For More Information:

Visit : www.icertglobal.com     Email : info@icertglobal.com

Big Data is a big component of our lives these days. It assists us in making choices and knowing how things function. It is an important task to learn from it. It is a type of job that is also relatively new, so there is a lot to learn. The best news is that there are fun and easy ways to learn by doing actual work. This can get you ready for a great job someday.

What is big data analysis?

Big Data Analysis is the examination of large amounts of information, or "big data," to determine patterns, trends, and interesting information. It can consist of such items as what people like, how markets change, and how people act. To examine big data, people use special tools such as statistics, machine learning, and data mining.

Big data comes from numerous sources, including social media, online purchases, online browsing, and intelligent devices (e.g., home assistants and fitness trackers). Big data is big, it comes in fast, and it can be very different from one piece to the next.

The main goal of Big Data Analysis is to allow individuals and companies to make informed decisions. It helps in planning, solving problems, working efficiently, and being ahead. Big data is used in different sectors like health care, finance, shopping, and delivery. It helps companies understand their customers, improve their work, and predict what will happen next.

This is what a Big Data Analyst typically does:

Working with Data

Data Cleaning and Collection: They collect information from various sources like corporate reports, social media, and smart devices. Then they clean the information to make it usable and accurate.

Organizing and Clustering Information : They employ equipment and computer software to search for, sort, and arrange the information making it understandable.

Identifying Patterns and Making Predictions : They use mathematics and particular models to study the data to see patterns and predict what could happen in the future.

What else is a big data analyst doing?

Big Data Analysts don't just see numbers—they use numbers to guide business decisions. And here's how they do it:

• Discovering and Sharing Ideas
• Understanding the Information
• They analyze the information to figure out trends and patterns. These can help companies make more informed plans and decisions.

Creating Reports and Charts:

• They translate the data into images such as graphs and charts. These are easier for everyone to comprehend. They also generate reports that interpret what the data signifies.
• Explaining to Others: They describe what they discovered in a simple way so that anyone—whether or not they know about data—can utilize and gain from the data.
• Aiding in Business Decisions

Providing Advice:

• They use the information to help the business make intelligent decisions—such as acquiring new customers, enhancing ads, or streamlining work.

Collaboration with Other Teams:

• They collaborate with units such as marketing or finance to ensure the information substantiates each aspect of the business.

Finding Solutions :

• If something goes wrong in the company, they study the data so that they can understand what went wrong and how to correct it.
• Learning and Enhancing

Staying Up to Date:

• Big Data keeps changing, and so analysts keep acquiring new tricks and tools to stay ahead.

Trying New Tools:

• They discover new methods to work smarter and faster, such as using improved software or more hip methods of presenting data.

What are they meant to learn?

To accomplish all of this, Big Data Analysts must:

• Be comfortable with working with tools such as SQL, Python, or R.

• Be a good problem-solver and pattern recognizer.

• Speak and write clearly so that others can understand your ideas.

• Pay attention to tiny details and enjoy learning something new.

Big Data Analyst Salaries

A Big Data Analyst's salary varies based on where he or she works, experience, and industry. Most companies are faced with data, so the work is fairly compensated in most regions. A summary of salaries worldwide is shown below:

United States (US):

U.S. Big Data Analysts receive good pay. The majority are between $70,000 and $115,000 per year. More experienced or specialized individuals may earn above $130,000.

India:

In India, this profession is increasing rapidly. Big Data Analysts get ₹4,00,000 to ₹10,00,000 annually. Those with extremely long experience or those who work in the most prominent companies earn even higher.

United Kingdom (UK):

In the UK, the majority of individuals make between £30,000 and £60,000 a year. Specialists carrying specialized equipment or skills may make £70,000 to £90,000 or more.

Europe:

European pay varies from country to country.

In the Netherlands, France, or Germany, you can earn between €40,000 and €70,000.

• With additional experience, it can exceed €80,000.

• In Norway or Sweden, for example, compensation could be greater due to higher living expenses.

How to become a Big Data Analyst ?

Desire to be a Big Data Analyst? Here's an easy-to-follow step-by-step guide:

1. Obtain the right education.

• Bachelor’s Degree: Start with a bachelor's in fields such as math, computer science, or business analytics.

• Courses and Certifications: Take additional classes or online classes in programs such as Hadoop, Spark, Python, SQL, and machine learning. These will differentiate you.

2. Acquire Technical Skills

• Encoding: Learn to program languages like Python, R, and Java.

• Databases: Learn how to use database systems, including SQL and NoSQL.

• Big Data Tools: Learn about big data tools like Apache Hadoop, Spark, and Kafka.

• Data Visualization: Learn to make dashboards and charts with Tableau, Power BI, or Matplotlib python libraries.

3. Get Real Experience

• Internships: Try to get an internship where you'll be able to work with real data. It is a valuable way of learning and networking with industry people.

• Tasks: Work on your personal projects or work on hackathons. You may also work on open-source projects.

4. Create a Portfolio

• Share Your Efforts: Develop a portfolio (collection) of your best work. Include graphs, reports, and whatever demonstrates how you perceive and interpret data.

5. Continuously Learn and Network

• Keep Learning: Big Data is constantly evolving. To remain current, take online courses, observe webinars, and attend workshops. Emerging tools and concepts are always being launched!

• Meet Other Professionals: Join clubs, go to conferences, and be part of online forums for Big Data. Discussion with people who work in your field can educate you further and lead you to find career opportunities.

6. Start Looking for Jobs

• Entry-Level Positions: Look for entry-level jobs, for example, Data Analyst, Junior Data Scientist, or Business Intelligence Analyst. They provide you with hands-on experience of handling data.

• Stand Out on Your Resume : Customize your resume and cover letter for every position you apply for. Emphasize your skills, education, and projects you have worked on.

How iCert Global Can Assist You ?

iCert Global offers special training courses for students interested in Big Data Analytics. They cover all aspects from basic ideas to advanced tools such as:

• Large data systems (like Hadoop and Spark)

• Coding (such as Python and R)

• Working with databases

• Developing charts and dashboards

• Machine learning basics

These courses are designed with the help of experts, so what you learn is usable in actual work.

How to obtain Big Data certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion.

Big Data is revolutionizing the world, and Big Data Analysts are the unsung heroes. They assist companies in making sound decisions by searching for patterns in enormous sets of data. If you like numbers, solving puzzles, and playing around with technology, this could be the perfect career for you!

If you have the right skillset, some experience with hands-on work, and some guidance through training programs like those at iCert Global, you can embark on the journey to becoming a Big Data Analyst. The future is full of data — and you can be the one who turns it into useful insights.

Begin learning today, and open up a future of promising possibilities in Big Data!

Contact Us For More Information:

Visit : www.icertglobal.com     Email : info@icertglobal.com

Understanding Data Governance in Simple Language

Data is akin to fuel for companies to run smoothly. Companies use data for a number of things. They get customer insights, make knowledgeable decisions, and track funds. But with so much data, there has to be regulation. Data Governance comes in here.

What is data governance?

Data Governance is a group of practices and regulations. It helps companies manage and secure their data well. It guarantees data is used properly, kept secure, and organized from the collection to disposal.

The Primary Components of Data Governance

1. Data Management Rules

Every business requires data management rules. The rules outline how data must be made accurate, safe, and in compliance. The rules assist in ensuring employees make the same efforts when working with data.

2. Data Managers

Data stewards are those people in a business. They make sure data is properly used.

They assist teams to comprehend and adhere to the company's data regulations. This makes it all work effectively.

3. Maintaining Data Accuracy

Good data management ensures all data is accurate and up-to-date. Businesses employ innovative methods to test data quality and fix mistakes. This enables businesses to make better decisions based on good information.

4. Data Protection and Confidentiality

It is very crucial to safeguard precious and confidential data. Data governance has security regulations. They do not allow data to be stolen, leaked, or misused. Businesses also adhere to regulations such as GDPR, CCPA, or HIPAA to ensure personal data security.

5. Companies build data catalogs

The catalogs describe and list all their data. It is simple to locate and utilize the data. Metadata management enables you to understand information regarding data. It informs you where data originates and how it is related to other data.

6. Managing Data from Beginning to End Data

governance keeps data under management from the time it is gathered to the time it is no longer required. Data lifecycle management ensures data is of value, secure, and compliant with legal needs.

7. Managing

Who Can See Data Not everybody should see all company information. Data governance manages who can see or modify data. It prevents valuable information from getting compromised and misuse.

8. Developing  

A Data Governance Plan and Team A successful data governance framework must have a clearly defined plan and leadership team. Most corporations begin with a Data Governance Council. This team creates rules, manages policies, and ensures data is used to fuel business growth.

9. Training of Employees

on Data Rules Employees must be trained on data handling. Training enables employees to learn data policies, security procedures, and best practices, which make them adhere to company policies.

10. Using Technology to Manage Data

Companies use technology to organize data and protect it. The technologies include data catalogs, security software, and access control systems. This configuration enables companies to observe, manage, and protect their data more conveniently.

11. Always Improving Data Management

Better data governance is not a one-time activity. It requires frequent updates.Firms prefer to change and update their data policy. They do so to maintain pace with new technology, security issues, and business regulations.

Advantages of Data Governance

1. Enhanced Quality of Data

 Data governance helps firms keep their data accurate, complete, and reliable. Firms can have faith in their data by implementing open rules of quality. They should correct mistakes in a timely manner and maintain consistency. This helps to make informed decisions, reports, and analyses.

2. More Robust Data Security

Cyber attacks and data breaches have the potential to damage a company's finances and reputation. Data governance employs security features such as encryption, access controls, and data masking to safeguard precious information from hackers and unauthorized users.

3.Strict government regulations

on businesses in dealing with data are established. GDPR, CCPA, and HIPAA are some examples. Data governance helps businesses comply with the law. This prevents the possibility of fines and lawsuits.

4. Enhanced Data Management

Enhanced data management prevents duplicate records and dispersed data. It enables organizations to better organize, store, and utilize data. This makes data available more quickly and saves storage capacity.

5. Better Decision-Making

Leaders can make better decisions for the business with correct and trustworthy data. Data governance provides decision-makers access to the right information, leading to better business performance and competitiveness.

6. Creating Revenue from Data

Companies can create revenue from their data. With proper management, firms can group, categorize, and use data in a productive manner, enabling them to sell data, create data-based products, or form smart partnerships.

7. Decreased Data Risks

Outdated or useless data increases the danger of security breaches and legal issues. Data governance establishes data deletion and retention policies. This reduces security threats.

8. Improved Teamwork

Data governance improves interdepartmental collaboration through defined roles and responsibilities. Data governance dispels team silos. Through this, all people adapt to uniform data administration practices.

9. Establishing Trust

 When companies manage their data in a good manner, it earns the trust of customers, partners, and stakeholders. Individuals are more inclined to engage with and recommend a company that values transparency and security.

10. Staying Ahead of Competitors

Companies that manage their data well can adjust to market changes more quickly, find new chances, and create new ideas with confidence. This gives them an advantage over competitors.

11. More Transparency

Data governance facilitates transparent documentation of data sources, modifications, and processes. It allows companies to view where the data originates from and how the data is processed. It keeps them accountable and transparent.

12. Enhanced Risk Management

By solving and correcting potential data issues ahead of time, businesses can reduce risks to security, regulations, and business processes. This makes data management more secure and efficient.

Top Data Governance Tools

1. IBM Data Governance

IBM provides robust data governance capabilities that enable companies to govern, secure, and capitalize on their data in the most effective way.

Main Points:

• Data Cataloging: It allows users to discover and understand data easily.

• Data Quality Management: Ensures data reliability and accuracy through error detection and correction.

• Metadata Management: Monitoring data sources and relationships for transparency.

• Policy and Governance Framework: Implements and applies data compliance policies and standards.

• Data Security: Shields precious information through encryption, access controls, and concealment techniques.

2. Ataccama

Ataccama is a specialist in data governance management, master data, and data quality. This guarantees organizations have compliant, accurate, and reliable data.

Main Points:

• Data Quality: Offers tools to test data, cleanse it, and standardize it to enhance precision.

• Master Data Management: Helps make one reliable source of data.

• Data Cataloging: Provides an exhaustive data catalog to make data easily available.

• Data Lineage: Allows users to trace data source and changes by mapping them.

• Governance Workflow: Automates data tasks, approvals, and policy management.

3. Alation

Alation specializes in data catalog and collaboration so that organizations can efficiently find, manage, and share data assets.

Main Points:

• Data Catalog: Secure search and discovery for convenient self-service data access.

• Data Profiling: Helps assess data quality and characteristics.

• Collaboration: Allows groups to collaborate, track usage, and comment on information.

• Data Governance Policies: Help set and apply policies and regulations on data handling.

• Integration: Seamlessly integrate with several data platforms and tools.

4. Informatica

Informatica is a leading company that assists in data governance and management. They offer a number of data integration, quality, and metadata management solutions.

Main Points:

• Data Integration: Sophisticated ETL (Extract, Transform, Load) capability for transferring and altering data.

• Data Quality: Software for validating, cleaning, and verifying data quality.

• Metadata Management: Complete tools for documenting and tracking data assets.

• Data Governance Framework: Assists in defining and applying governance rules, policies, and workflows.

• Data Security: Protects valuable data with robust security measures.

5. Collibra

Collibra is a top-ranked data governance platform. It deals with data cataloging, data lineage, and stewardship. This assists with proper data handling and compliance.

Main Points:

• Data Catalog: Assist organizations in cataloging, classifying, and governing data. It is readily accessible and comprehendible.

• Data Lineage: Shows how data travels between systems, helping you follow data movements.

• Data Stewardship: Assigns data ownership and accountability towards better governance.

• Governance Workflow: Comprises process automation and approval stages for data rule handling.

• Privacy & Compliance: Helps organizations to meet data privacy laws like GDPR, CCPA, and HIPAA.

6. OvalEdge

OvalEdge offers data cataloging, data lineage, and governance capabilities. These enhance data management in terms of efficiency and simplicity.

Main Points:

• Data Catalog: A straightforward application for locating, describing, and tracking data assets.

• Data Lineage: Enables users to track data origins and dependencies.

• Governance Framework: Helps firms establish rules and procedures to follow.

• Collaboration: Enables team coordination and sharing of information on data.

• Integration: Integrate with multiple data platforms and databases to create one view of data.

Challenges with Data Management

• Data Complexity: Companies deal with a lot of disparate data, complicating governance.

• Ownership of Data: It is not clear who owns specific data assets.

• Resistance to Change: Employees can resist new governance streams and policies.

• Cost and Resources: Implementation of governance requires time, money, and human resources.

• Regulatory Changes: Companies ought to remain current with evolving data privacy and compliance regulations.

Data Management and Data Governance

Data Management

• A policy framework, procedure, and regulations for correct data use, security, and compliance.

• Sets norms for data ownership, stewardship, and quality within an organization.

• Emphasizes discussing accountability and guidelines for safeguarding data and privacy.

• Utilizes governance committees to manage data rules and plans.

• Maintains data consistency, compliance, and security across all departments.

Managing data

• The processes of collecting, storing, organizing, and maintaining information.

• Covers data architecture, integration, modeling, and database administration.

• Ensures data cleaning, transformation, and migration to ensure accuracy.

• Enables efficient data accessibility and availability for business operations.

• Provides the infrastructure required to manage governance policies effectively.

How to obtain  Big Data Certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Data governance enables companies to utilize data in an intelligent manner. Data governance keeps data safe and obeys the regulation. Data governance improves decision-making, maintains data accuracy, and establishes trust. It may be difficult to build a business, but the advantage is positive. Data engineers are in greater demand. More organizations need specialists to take care of their data.

Looking to begin or continue your data engineering career? Enroll in iCert Global's Data Engineering Program to acquire the skills you require to succeed.

FAQs

1. Who oversees data governance?

 Several individuals within a firm are accountable for data governance. A Data Governance Council develops the policies, and data stewards assist in enforcing them. Chief Data Officers (CDOs) and other professionals also ensure data is applied properly.

2. How is data governance beneficial to security and privacy?

It secures crucial data by having rules over who gets to access or utilize it. It assists businesses with abiding by regulations on protecting individual data.

3. In what ways does technology support data governance?

There is specific software that assists companies in organizing, securing, and monitoring data. Such software programs also search for errors and verify proper use of data.

Contact Us For More Information:

Visit : www.icertglobal.com     Email : info@icertglobal.com

Data has undergone a tremendous change over time. In the beginning, people cared about gaining useful information. But in recent years, everyone has come to understand the importance of managing data properly. Because of this, the importance of data engineers has become tremendous.

What is a Data Engineer?

Data engineers assist in gathering, storing, and structuring data to be utilized in various analyses. They construct and maintain the infrastructure through which data can be utilized by businesses. Simply put, data engineers transform raw data into actionable information, so they are extremely crucial to data-driven decision-making.

Data Engineer Responsibilities and Tasks

1. Collecting and Combining Data

Data engineers gather data from a number of sources including websites, databases, and web portals. They develop systems that smoothly transfer the data into storage so that everything is ready and accessible for use.

2. Keeping and Handling Data

After the data is gathered, data engineers decide where and how to store it. They choose the right databases, organize the data, and ensure it is accurate and trustworthy. They also ensure that the system will be able to handle a large amount of data without any slowdown.

3. ETL (Extract, Transform, Load) Processes

ETL is a key component of data engineering. It cleanses and structures raw data for analysis. It ensures data is in the correct format and of use to scientists and analysts.

4. Big Data Management

Companies deal with a lot of data, so data engineers work with specialized tools such as Hadoop and Spark. This allows them to process and analyze large amounts of data efficiently and quickly.

Data Engineer Careers and Responsibilities

5. NoSQL Databases

NoSQL databases such as MongoDB and Cassandra are used by data engineers, along with regular databases. Databases are ideal for storing and processing data that does not have a predetermined structure.

6. Cloud Computing

Cloud providers like AWS, Azure, and Google Cloud enable businesses to host and process information on the cloud. Data engineers utilize these cloud services to build systems that can scale seamlessly and lower costs.

7  Big Data Systems.

Data engineers use systems that share data among many computers. This helps to deal with large amounts of information and keeps everything running as it should, even in the case of a problem.

8. Processing Data Immediately

There are some sectors that require data to be processed in real-time. Data engineers apply tools such as Apache Kafka to collect and process data as it comes in, enabling organizations to make immediate decisions.

Skills Needed to Become a Data Engineer

1. Programming

Data engineers should be knowledgeable about programming languages like Python, Java, or Scala. These programming languages assist them in designing data systems, organizing information, and automating tasks.

2. Databases

There is a need to understand different types of databases. Some like MySQL and PostgreSQL store data in tables. Some like MongoDB and Cassandra store data that is less structured. Data engineers need to pick the correct one for their job.

3. Big Data

Big data tools like Hadoop, Spark, and Hive allow data engineers to process large amounts of data quickly and efficiently.

4. ETL Tools

ETL software like Apache Nifi, Talend, and Apache Airflow support data movement and cleaning. Data engineers use these software packages to clean the data and prepare it for use.

5. NoSQL Databases

Some information is not easily tabular. NoSQL databases assist in storing and handling such information. Data engineers should understand when to utilize them.

6. Cloud Computing

Cloud platforms such as AWS, Azure, and Google Cloud allow businesses to store and operate on data digitally. Data engineers must know how to utilize these platforms.

7. Handling Large Systems

Data engineers must be able to construct systems that can process lots of information without collapsing. Such systems assist companies in handling and processing data in a reliable manner.

8. • Hadoop

Hadoop is one of the most important tools used to handle big data. Data engineers must know how to operate Hadoop and its modules, including HDFS and MapReduce, to store and handle huge data.

9• Kafka

Most companies require immediate processing of data. Apache Kafka is one tool that assists in processing data in real time. Data engineers should know how to utilize it.

10 • Python

Python is a popular programming language used in data engineering. It helps in tasks like scripting, data handling, and process automation.

11 • SQL

SQL is a valuable skill for data engineers. It enables them to interact with databases by querying to store, arrange, and retrieve data easily.

12 • Data Warehousing

A data warehouse is an infrastructure for gathering and aggregating data from multiple sources. Data engineers should be able to build and run these systems so that they are able to assist businesses in making decisions.

13 • Data Architecture

Data engineers plan systems for data storage and movement in an efficient manner. Data engineers need to know how data moves, where data is stored, and how applications retrieve it.

14 • Coding

Data engineers need to possess good programming skills to connect databases with websites, applications, and other software systems. Training in Java, C#, Python, or R can be very helpful.

15 • Computer Systems

Knowledge of operating different operating systems, such as UNIX, Linux, and Windows, is required to manage data systems and make them run smoothly.

16 • Apache Hadoop Analytics

Apache Hadoop is a tool that helps store and manage big data on many computers. It is used for data processing, storing, securing, and sorting. Studying Hadoop, HBase, and MapReduce can make you a better data engineer.

17. • Machine Learning

Machine learning is applied primarily to data science but is also applicable to data engineers. Understanding how data is being used for analysis and prediction can help in building improved data systems.

How Do Data Engineers Assist Organizations?

Data engineers design and maintain systems that collect, store, and arrange data. They make sure that organizations have reliable data to make sound decisions. That is how they assist:

• Construction of Data Pipelines – Data engineers create systems for moving data from sources to storage, making the data accessible. The process enables well-informed decision-making in companies.

• Data Quality Assurance – They cleanse and validate data to make it accurate and uniform so that analysts can trust the information.

• Scaling Systems – As companies grow, they collect more data. Data engineers build systems that are able to handle large amounts of data without slowing down.

• Minimizing Bias in Data – They make sure data processes are transparent and unbiased, preventing biased data analysis and machine learning.

• ETL (Extract, Transform, Load) Processes – Data engineers convert raw data into a structured format, thus making it convenient for analysts and scientists to study it.

• Securing Data – They enact security controls to safeguard valuable information and keep pace with privacy legislation.

How can I become a data engineer?

1. Education – Start by studying computer science, software engineering, or a related discipline. You typically require a bachelor's degree.

2. Learn Programming – Master the programming languages like Python, Java, or Scala. Also, learn SQL to work with databases.

3. Learn about Databases – Learn to manage various databases, such as MySQL and PostgreSQL (for structured data) and MongoDB or Cassandra (for unstructured data).

4. Know Big Data Tools – Learn big data tools such as Hadoop, Spark, and Apache Kafka, which help handle large data.

5. Study ETL Tools – ETL tools like Apache Nifi and Apache Airflow assist in transporting and structuring data. You must understand how to utilize them.

6. Learn Cloud Platforms – Companies store most data in the cloud. Learn AWS, Azure, or Google Cloud.

7. Use Version Control – Software like Git allows you to keep track of code and collaborate with teams. Knowing Git is a useful skill.

8. Learn About Data Warehouses – Learn about data storage systems such as Amazon Redshift or Google BigQuery, which allow companies to store and utilize their data.

Data Engineer Career Path

1. Junior Data Engineer – A starting job where you get to learn the fundamentals of data engineering

2. Data Engineer – You create and manage data pipelines that transfer and structure data.

3. Senior Data Engineer – You work with more advanced data systems and assist junior engineers.

4. Data Engineering Manager – You lead a team of data engineers and work on big projects.

5. Solution Architect – You design the entire data system for an organization so that everything works smoothly.

Data Engineer Salary

Data engineers are in demand, and their pay varies based on experience and location:

• Junior Data Engineer – Earns between $60,000 and $100,000 per year.

• Mid-Level Data Engineer – Earns between $90,000 and $130,000 annually.

• Senior Data Engineer – Earning between $120,000 and $180,000 or more a year.

How to obtain Data Engineer Certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

How to Become a Certified Data Engineer

Becoming certified can demonstrate your ability and make you a more desirable candidate when you are job hunting. Some solid options are:

• AWS Certified Data Analytics – It is all about data engineering on AWS.

• Google Cloud Professional Data Engineer – Manages data engineering on Google Cloud.

• Microsoft Certified: Azure Data Engineer Associate – Teaches data engineering using Microsoft Azure.

• Cloudera Certified Data Engineer – Expert in big data technology.

FAQs

1. What are the new trends in data engineering?

Some of the interesting data engineering trends are:

Serverless computing involves leveraging cloud services to process information without server administration.

• Real-time data pipelines – Moving and processing data in real-time as it is being created.
• AI and ML integration - Application of machine learning and artificial intelligence in order to improve data processing.
• Data mesh architecture – A novel approach to organize and share data across big companies.

2. How do data engineers assist AI and ML projects?

Data engineers make sure that AI and ML initiatives are well-stocked with the right data to utilize. They:

• Establish robust data pipelines to transfer and structure data.
• Maintain data quality such that AI systems learn from clean data.
• Improve data storage so data scientists can access what they require quickly.

3. Should a data engineer know SQL?

• Yes! SQL is quite crucial to data engineers. It assists them:
• You can search for and manage data in databases.
• Convert data to make it valuable for analysis & Maintain clean and organized data in pipelines.

Contact Us For More Information:

Visit : www.icertglobal.com     Email : info@icertglobal.com

Big Data is omnipresent, and it is really essential to capture and store all the data being generated, so we do not miss out on something significant. There is ample data out there, and what we do with it is most important. That is why Big Data Analytics is really crucial in technology. It assists businesses in making improved decisions and provides them with a competitive advantage. This is applicable for businesses as well as individuals working in the Analytics sector. For instance, individuals who understand Big Data and Hadoop have a plethora of job opportunities. For instance, in the online lending sector, Big Data assists lenders in making faster and better decisions, which enhances the experience of individuals who apply for a loan.

Why Big Data Analytics is a Wonderful Career Choice ?

If you are still in doubt as to why Big Data Analytics is a sought-after skill, here are 10 additional reasons to help you comprehend it better.

1. Analytics Workers: As Jeanne Harris, a senior Accenture Institute for High Performance executive, says, "data is useless without the skill to analyze it." Today, there are more Big Data and Analytics careers than ever. IT professionals are prepared to spend money and time to learn these skills because they realize it is a good career choice for the future.

The need for data professionals will only just start. Srikanth Velamakanni, ceo and cofounder of Bangalore-based Fractal Analytics, forecasts that in the next few years, the analytics market will grow to at least one-third the size of the worldwide IT market, now one-tenth.

Technology experts who are experienced in analytics are in demand because companies want to take advantage of Big Data. Job listings for analytics on platforms like Indeed and Dice have increased dramatically in the past year. Other job platforms are seeing the same growth. This is happening because more companies are beginning to use analytics and need staff with such knowledge.

QuinStreet Inc. carried out a survey and found that Big Data Analytics is becoming highly important for the majority of firms in America. They are either already using it or planning to use it in the next two years. If you want to learn more about Big Data and how it is being applied, you can look at online Data Engineering Courses.

2. Large Job Opportunities and the Closing of the Skills Gap:

There is more demand for analytics professionals, but no one with the right training is around to meet that demand. It is occurring everywhere globally and not in a particular location. Although Big Data Analytics is a lucrative job, most of the jobs remain unfilled because there are not enough professionals who have the right education. McKinsey Global Institute reports that by 2018 America will require nearly 190,000 data scientists and 1.5 million managers and analysts who have the ability to comprehend and make decisions from Big Data.

In case you want to know more about Data Science, you can register for a live Data Science Certification Training with iCert Global, which comes with 24/7 support and lifetime access.

3. Salary Details:

Its high demand for Data Analytics experts is driving salaries upwards for competent staff, making Big Data a viable career for those who have the right skills. It is occurring on a global scale, as nations such as Australia and the U.K. are witnessing staggering salary growth.

The Institute of Analytics Professionals of Australia (IAPA) 2015 Skills and Salary Survey Report indicates that data analysts have an average salary of $130,000 per year, an increase of 4% from the previous year. The average data analyst salary for the past few years has been around 184% of the average full-time employee salary in Australia. The outlook for analytics professionals can also be estimated from the membership of IAPA, which has reached over 5,000 members in Australia since its founding in 2006.

Randstad cites that the increase in yearly salary for Analytics employees in India is, on average, 50% more than other IT employees. As per the Great Lakes Institute of Management Indian Analytics Industry Salary Trend Report, salaries for analytics personnel in India increased by 21% in 2015 from 2014. As per the report, 14% of overall analytics personnel earn more than Rs. 15 lakh per annum.

The U.K. salary trend for Big Data Analytics is also increasing very fast and positively. A search on Itjobswatch.co.uk in early 2016 indicated that the average salary for Big Data Analytics job advertisements was £62,500, whereas in early 2015 it was £55,000 for the same jobs. The salary increased 13.63% year on year.

4. Big Data Analytics: Extremely Critical for Most Organizations

The 'Peer Research – Big Data Analytics' report indicates that big data analytics is extremely crucial to the majority of organizations. They believe it assists them in improving and succeeding.

Based on the survey answers, approximately 45% of the respondents hold the view that Big Data Analytics will help companies gain better insights. Another 38% wish to utilize Analytics to uncover sales and market opportunities. Over 60% of the respondents are utilizing Big Data Analytics to leverage their social media marketing. QuinStreet's findings also show that Analytics is extremely significant, with 77% of individuals stating that Big Data Analytics is extremely significant.

A Deloitte survey, titled Technology in the Mid-Market; Perspectives and Priorities, says that most leaders perceive the value of analytics. Based on the survey, 65.2% of the individuals already use some form of analytics to advance their businesses. The following image depicts their strong faith in Big Data Analytics. To learn more about Big Data and its application, refer to the Azure Data Engineering Course in India.

5. There are more Big Data Analytics users:

New technologies are making it easier for individuals to conduct sophisticated data analysis on large and varied sets of data. It has come to be learned through a survey conducted by The Data Warehousing Institute (TDWI) that over one-third of the individuals surveyed are already using advanced analytics on Big Data for purposes like Business Intelligence, Predictive Analytics, and Data Mining.

Big Data Analytics enables organizations to perform at a higher level than their peers, and thus, there are increasingly more companies that are beginning to utilize the appropriate tools at the appropriate time. Most of the respondents to the 'Peer Research – Big Data Analytics' survey already have a plan to implement these tools. The others are working hard to develop one.

The Apache Hadoop framework is the most popular. There is a paid version and a free version, and companies select the one they prefer. More than half of the respondents have started using or plan to use a version of Hadoop. Of these, a quarter have chosen the free, open-source version of Hadoop, which is twice the number of companies that chose the paid version.

6. Analytics: A Key Component of Decision Making

Analytics is the most critical for the majority of organizations, and everyone is unanimous on this point. As per the 'Analytics Advantage' survey, 96% of respondents are of the opinion that analytics will be more critical in the next three years. This is because there is plenty of untapped data, and currently, only basic analytics is being executed. Around 49% of the individuals interviewed firmly opined that analytics makes better decisions. Another 16% opine that it's great to make better crucial plans.

7. The Emergence of Unstructured and Semistructured Data Analytics

The 'Peer Research – Big Data Analytics' survey indicates that businesses are rapidly expanding the application of unstructured and semistructured data analytics. 84% of the participants reported that their businesses already process and analyze unstructured data like weblogs, social media, emails, photos, and videos. The other participants reported that their businesses will begin to use these sources of data within the next 12 to 18 months.

8. Big Data Analytics is Used Everywhere!

There exists a huge demand for Big Data Analytics due to its incredible characteristics. Big Data Analytics is growing as it is applied to numerous different fields. The following is the image depicting the job opportunities in various fields where Big Data is applied.

9. Defying Market Projections for Big Data Analytics

Big Data Analytics has been labeled the most disruptive technology by the Nimbus Ninety survey. That is, it will make a huge impact within the next three years. There are some other market forecasts that confirm the same:

•  The IIA says Big Data Analytics solutions will help enhance security by utilizing machine learning, text mining, and other mechanisms to predict, identify, and circumvent threats.

A survey named The Future of Big Data Analytics – Global Market and Technologies Forecast – 2015-2020 shows that the global market is expected to grow by 14.4% annually from 2015 through 2020.

•  The Apps and Analytics Technology Big Data Analytics market is projected to grow at 28.2% annually, Cloud Technology at 16.1%, Computing Technology at 7.1%, and NoSQL Technology at 18.9% during the same period.

10. Many Options of Job Titles and Analytics Types:

From a career point of view, there are numerous options for the job that you do and the industry you do it in. Since Analytics is applied to numerous industries, there are numerous different job roles to choose from, including:

• Big Data Analytics Business Consultant

• Big Data Analytics Architect

• Big Data Engineer

• Big Data Solution Architect

• Big Data Analyst

• Analytics Associate

• Business Intelligence and Analytics Consultant

• Metrics and Analytics Specialist

Big Data Analytics professions are diverse, and you can select any of the three forms of data analytics depending on the Big Data environment:

• Prescriptive Analytics

• Predictive Analytics

 • Descriptive Analytics

Numerous organizations, such as IBM, Microsoft, Oracle, and more, are applying Big Data Analytics to their business requirements. Due to this, numerous job opportunities exist with these organizations. Conclusion: Although analytics may be tricky, it does not eliminate the requirement for human judgment. That is, in fact, businesses require professionals with analytics certification to interpret data, consider business requirements, and deliver actionable insights. That is why professionals with analytics certification are in huge demand since companies desire to utilize the advantages of Big Data. If you wish to be a specialist, you may enroll in courses such as iCert Global's Data Architect course. The course trains in Hadoop, MapReduce, Pig, Hive, and many more. A professional with analytical skills can comprehend Big Data and become a productive employee of a business, contributing to their career and the business as well.

How to obtain Cloud Computing certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion:

In conclusion, Big Data is becoming a huge part of many industries, creating lots of job opportunities and offering great salaries for people with the right skills. As businesses rely more on data to make important decisions, there is a growing demand for professionals who can analyze and understand that data. By learning about Big Data and getting certified, such as through courses like iCert Global’s Data Architect course, you can unlock many career paths in tech and business. So, if you're interested in working with data and solving problems, Big Data is a great field to explore for your future career!

Contact Us For More Information:

Visit : www.icertglobal.com     Email : info@icertglobal.com

Data engineering projects can be complex and require proper planning and collaboration. To achieve the best outcome, it is necessary to define precise objectives and have a clear understanding of how each component works in conjunction with one another.

There are a lot of tools that assist data engineers in streamlining their work and ensuring that everything goes smoothly. But despite these tools, ensuring that everything works correctly still consumes a lot of time.

What Is Data Engineering?

Data engineering refers to structuring and preparing data. This makes it easy for other systems to utilize it. It usually involves making or modifying databases. You also need to have the data ready to use whenever you need it, regardless of how it was gathered or stored.

Data engineers examine data to discover patterns. They apply these findings to develop new tools and systems. They assist companies by transforming raw data into valuable information in the form of reports.

Top 10 Data Engineering Projects

Project work assists beginners in learning data engineering. It allows them to apply new skills and create a portfolio that impresses employers. Below are 10 data engineering projects for beginners. Each project has a brief description, objectives, skills you will acquire, and the tools you can use.

1. Data Collection and Storage System

Project Overview: Develop a system to collect data from websites and APIs. Clean the data and store it in a database.

Goals:

• Learn how to collect data from different sources.
• Understand how to clean and prepare data.
• Store data in a structured way using a database.

Skills You’ll Learn: API usage, web scraping, data cleaning, SQL.

Tools & Technologies: Python (Requests, BeautifulSoup), SQL databases (MySQL, PostgreSQL), Pandas.

2. ETL Pipeline

Project Overview: Build an ETL (Extract, Transform, Load) pipeline. This pipeline will take data from a source, process it, and then load it into a database.

Goals:

• Understand ETL processes and workflows.
• Learn how to change and organize data.
• Automate the process of moving data.

Skills You’ll Learn: Data modeling, batch processing, automation.

Tools & Technologies: Python, SQL, Apache Airflow.

3. Real-Time Data Processing System

Project Overview: Develop a system to handle live data from social media and IoT devices.

Goals:

• Learn the basics of real-time data processing.
• Work with streaming data.
• Perform simple analysis on live data.

Skills You’ll Learn: Stream processing, real-time analytics, event-driven programming.

Tools & Technologies: Apache Kafka, Apache Spark Streaming.

4. Data Warehouse Solution

Project Overview: Create a data warehouse. It will collect data from various sources. This makes reporting and analysis easy.

Goals:

• Learn how data warehouses work.
• Design data structures for organizing and analyzing data.
• Work with popular data warehouse tools.

Skills You’ll Learn: Data warehousing, OLAP (Online Analytical Processing), data modeling.

Tools & Technologies: Amazon Redshift, Google BigQuery, Snowflake.

5. Data Quality Monitoring System

Project Overview: Create a system to identify and report data problems. This includes missing values, duplicate records, and inconsistencies.

Goals:

• Understand why data quality is important.
• Learn how to track and fix data problems.
• Create reports to monitor data quality.

Skills You’ll Learn: Data quality assessment, reporting, automation.

Tools & Technologies: Python, SQL, Apache Airflow.

6. Log Analysis Tool

Project Overview: Build a tool to analyze log files from websites or apps. This tool will help identify patterns in user behavior and system performance.

Goals:

• Learn to read and analyze log data.
• Identify trends and patterns.
• Show results using data visualization.

Skills You’ll Learn: Log analysis, pattern recognition, data visualization.

Tools & Technologies: Elasticsearch, Logstash, Kibana (ELK stack).

7. Recommendation System

Project Overview: Create a system that recommends items to users. It will use their past choices and preferences from similar users.

Goals:

• Understand how recommendation algorithms work.
• Use filtering techniques to suggest relevant content.
• Measure how effective your recommendations are.

Skills You’ll Learn: Machine learning, algorithm implementation, evaluation metrics.

Tools & Technologies: Python (Pandas, Scikit-learn), Apache Spark MLlib.

8. Sentiment Analysis on Social Media Data

Project Overview: Develop a tool that analyzes social media posts. It will classify them as positive, negative, or neutral.

Goals:

• Work with text-based data.
• Learn how sentiment analysis works.
• Display the results visually.

Skills You’ll Learn: Natural Language Processing (NLP), sentiment analysis, data visualization.

Tools & Technologies: Python (NLTK, TextBlob), Jupyter Notebooks.

9. IoT Data Analysis

Project Overview: Analyze data from smart devices (like home sensors) to find usage trends, detect unusual activity, or predict maintenance needs.

Goals:

• Handle data from IoT devices.
• Work with time-series data.
• Detect issues and predict trends.

Skills You’ll Learn: Time-series analysis, anomaly detection, predictive modeling.

Tools & Technologies: Python (Pandas, NumPy), TensorFlow, Apache Kafka.

10. Climate Data Analysis Platform

Project Overview: Create a system to gather, process, and display climate data. This will help us spot trends and unusual patterns.

Goals:

• Work with large climate datasets.
• Learn to visualize environmental data.
• Present complex data in an easy-to-understand way.

Skills You'll Acquire: Data processing, visualization, environmental analysis.

Tools & Technologies: Python (Matplotlib, Seaborn), R, D3.js.

How to obtain Quality Managemt certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2024 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Want to grow professionally in data engineering? The Professional Certificate Program in Data Engineering from iCert Global and Purdue University enables you to become proficient in big data, cloud computing, and data pipelines.

Develop skills in Apache Spark, Hadoop, AWS, and Python. Do so through hands-on projects, live case studies, and training by experts. This certification develops your skills and increases your credibility as a software professional, data engineer, or data analyst. You can become a top talent in the industry through it.

Contact Us For More Information:

Visit : www.icertglobal.com     Email : info@icertglobal.com

Every time you use the internet to learn about something, make an online payment, order food, or do anything else, data is created. Social media, online shopping, and streaming videos have all contributed to a huge increase in the amount of data we generate. To make sense of all this data, we use something called data processing. Let’s explore what data processing is and how it works.

What Is Data Processing?

Raw data, or data in its unorganized form, isn’t very helpful to anyone. Data processing is the process of turning this raw data into useful information. This is done in a series of steps by a team of people, like data scientists and engineers, who work together in a company. First, the raw data is collected. Then, it’s filtered, sorted, analyzed, and stored before being shown in an easy-to-understand format.

Data processing is very important for businesses because it helps them make better decisions and stay ahead of the competition. When the data is turned into charts, graphs, or reports, people in the company can easily understand and use it.

Now that we know what data processing is, let’s look at how the data processing cycle works.

Step 1: Collection

The first step in the data processing cycle is collecting raw data. The type of data you gather is really important because it affects the final results. It’s important to get data from reliable and accurate sources so the results are correct and useful. Raw data can include things like money numbers, website information, company profit or loss, and user activity.

Step 2: Preparation

Next comes data preparation, also known as data cleaning. This is when the raw data is sorted and checked to remove mistakes or unnecessary information. The data is checked for errors, duplicates, missing details, or wrong information. The goal is to make sure the data is in the best possible shape for the next steps. By cleaning up the data, we get rid of anything that could mess up the final results, ensuring that only good quality data is used.

Step 3: Input

Once the data is ready, it has to be turned into a format that computers can understand. This is called the input step. The data can be entered into the computer using a keyboard, scanner, or other tools that send the data to the system.

Step 4: Data Processing

This step is when the actual work happens. The raw data is processed using different methods like machine learning or artificial intelligence (AI) to turn it into useful information. Depending on where the data is coming from (like databases or connected devices) and what it will be used for, the process might look a little different.

Step 5: Output

After processing, the data is shown to the user in an easy-to-understand form, like graphs, tables, videos, documents, or even sound. This output can be saved and used later in another round of data processing if needed.

Step 6: Storage

The final step is storing the data. In this step, the processed data is saved in a place where it can be quickly accessed later. This storage also makes it easy to use the data again in the next data processing cycle.

Now that we understand data processing and its steps, let's take a look at the different types of data processing.

Data Processing is the way we take raw data (like numbers, facts, or information) and turn it into something useful, like a report or an answer. It helps us organize, sort, and understand the data better.

Understanding Data Processing and Its Different Types

Types of Data Processing:

1. Manual Data Processing:
   • This is when people process data by hand, like writing things down on paper or doing math on a calculator.
   • Example: Doing math homework without a computer.
2. Mechanical Data Processing:
   • This uses simple machines, like early calculators or typewriters, to help process data.
   • Example: Using a basic adding machine to do math.
3. Electronic Data Processing:
   • This is when computers and software are used to process data quickly and accurately.
   • Example: Using a computer to calculate grades in a school.
4. Real-time Data Processing:
   • Data is processed immediately as it happens.
   • Example: Watching live sports scores online.
5. Batch Data Processing:
   • Data is collected and processed all at once, instead of right away.
   • Example: Doing everyone's school grades at the end of the semester.
6. Distributed Data Processing:
   • This is when data is processed by multiple computers working together.
   • Example: Using cloud storage where data is stored and processed on many different computers.
7. Online Data Processing (OLTP):
   • Data is processed as soon as it's entered into a system, like when you buy something online.
   • Example: Making an online purchase where your payment is processed right away.

What is Data Processing: Methods of Data Processing

There are three main ways to process data: manual, mechanical, and electronic.

Manual Data Processing

Manual data processing is done completely by hand. People collect, filter, sort, and calculate the data without using any machines or software. It’s a low-cost method that doesn’t need special tools, but it has some downsides. It can lead to a lot of mistakes, take a lot of time, and require a lot of work from people.

Mechanical Data Processing

In mechanical data processing, simple machines and devices are used to help process the data. These could include things like calculators, typewriters, or printing presses. This method has fewer mistakes than manual processing but can still be slow and complicated when there’s a lot of data.

Electronic Data Processing

This is the most modern way to process data, using computers and software programs. Instructions are given to the software to process the data and create results. Although it’s the most expensive method, it’s also the fastest and most accurate, making it the best option for large amounts of data.

Examples of Data Processing

Data processing happens all around us every day, even if we don’t notice it. Here are a few real-life examples:

• A stock trading software turns millions of pieces of stock data into a simple graph.
• An online store looks at what you’ve searched for before to recommend similar products.
• A digital marketing company uses information about people’s locations to create ads for certain areas.
• A self-driving car uses data from sensors to spot pedestrians and other cars on the road.

Moving From Data Processing to Analytics

One of the biggest changes in today’s business world is the rise of big data. Although managing all this data can be tough, the benefits are huge. To stay competitive, companies need to have a good data processing plan.

After data is processed, the next step is analytics. Analytics is when you find patterns in the data and understand what they mean. While data processing changes the data into a usable format, analytics helps us make sense of it.

But no matter what process data scientists are using, the huge amount of data and the need to understand it means we need better ways to store and access all that information. This leads us to the next part!

How to obtain Bigdata certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion  

The future of data processing can be summed up in one short phrase: cloud computing.

While the six steps of data processing stay the same, cloud technology has made big improvements in how we process data. It has given data analysts and scientists the fastest, most advanced, cost-effective, and efficient ways to handle data. So, the same technology that helped create big data and the challenges of handling it also gives us the solution. The cloud can handle the large amounts of data that are a part of big data.

Contact Us For More Information:

Visit :www.icertglobal.com Email : 

A Product Owner has the responsibility to ensure a project is successful in Scrum. They work with the "product backlog." This is a list of tasks needed to improve or finish the product. This ensures that the product contains the greatest value for customers. Scrum is part of Agile. It helps teams communicate and collaborate better.

The Product Owner is a key member of the Scrum team. Their main job is to outline what the product should be and create the product backlog. They are the go-to person for the development team. They share what features the product needs based on customer requests. The Product Owner makes sure the development team knows what is most important to work on. They also resolve any questions that the team may have regarding what must be put in the product. The Product Owner makes sure the product being developed gives great value to its users.

What Does a Product Owner Do ?

A Product Owner decides what a product should be and how it works. They base these decisions on the needs of customers and key people, known as stakeholders. They collect data from research to determine what features are most essential.

The Product Owner creates a list called the "product backlog." This list includes all the required features and tasks for the product. They also prioritize the items on this list. They keep this list updated. They change it based on customer feedback and changing business needs. The Product Owner works closely with developers, designers, and marketers. They make sure the product is on time, meets customer needs, and stays within budget.

Product Owner Roles

The main job of a Product Owner is to make sure that product development creates the most value for the company. This means working closely with the development team. We want to make sure the product meets the right specifications and is finished on time.The Product Owner manages the product backlog.

This is a list of tasks the team needs to complete. Here's what they do with the backlog:

•Ensure the backlog is well-defined and everything written clearly.

•Prioritize activities such that the high-priority tasks are executed first.

•Ensure the work meets the customer's expectations and goals.

•Constant feedback to the development team.

• Ensure that all the team members know what is to be done.

Product Owner Skills

Some of the most important skills a Product Owner must possess are:

• Domain Knowledge: The Product Owner needs to know the field and how users will use the product.

• Leadership and Communication: They must be able to communicate effectively with all the stakeholders and guide the team towards the product objectives.

• Optimizing Value: The Product Owner must make sure the product gives the most value to customers quickly.

•Reading Customer Needs: They must translate what the customer is looking for and ensure that the development team is aware of these needs.

Product Owner's Responsibilities

•Product Backlog: The Product Owner maintains and creates the product backlog. The list must be prioritized according to importance and urgency. The backlog is frequently updated as the product evolves.

• Stages of Development: The Product Owner stays involved in product development. They update the team on any changes in customer needs or product goals. They join meetings to review progress and look for ways to improve.

•Serving as a Single Point of Contact: The Product Owner is the single point of contact for any inquiries regarding the product, ensuring everyone is aligned.

•Customer Goals Communication: The Product Owner must communicate the customer's requirements clearly to all the stakeholders in the project.

•Preempting Customer Needs: They should be capable of guessing what the customer will require next, even before the customer himself asks for it, looking at market dynamics and the customer journey.

•Progress Evaluation: The Product Owner reviews every step of the product development process and provides feedback on how it can be better.

Product Owner Skills

You must possess a combination of skills to be a successful Product Owner that will guide you in managing a product from the conception phase right up until it's released to customers.

Some of the key skills you should possess as a Product Owner include:

•Product Management

The Product Owner must be able to determine what features and requirements are most vital for the product. They must also know what customers need and identify opportunities in the market for new product concepts.

•  Agile Development

A Product Owner must know how Agile development is done. This involves practices such as Scrum, Kanban, and Lean. Knowing these practices will enable the Product Owner to prioritize the product backlog (the things to do list), schedule reviews, and cooperate with the development team.

Product Owner Stances

A Product Owner plays many key roles to help ensure a successful product. Six significant stances (or roles) a Product Owner can take are as follows:

1. Visionary

•The Product Owner develops and communicates a clear product vision that aligns with the company's objectives.

•They generate new product ideas and ensure everyone is aware of and believes in these ideas.

• They prioritize both short- and long-term objectives, deciding what will benefit the product and the organization down the road.

2. Collaborator

- The Product Owner teams up with developers, designers, and marketers. This way, they make sure the product is built right and delivered on schedule.

• They ensure that the team communicates effectively, fostering trust and collaboration among members.

• They involve everyone in sharing ideas and giving feedback, which helps the product.

3. Customer Representative

• The Product Owner represents the customer. They make sure the customer's needs and expectations are part of the product plan.

• They understand customer needs and wants. They use this info to guide the product.

•They seek customers' feedback and others' viewpoints to constantly improve the product.

4. Decision Maker

• The Product Owner makes key decisions about the product plan, what to build next, and how to use resources.

• They use data and feedback to make smart choices. They also check how the product is doing.

•They mediate conflicting requirements from customers, the team, and other parties.

5. Experimenter

• Product Owner encourages the team to try and test ideas to find what works best.

•They employ data and feedback to assist with decision-making and product improvement.

•They facilitate the team to test concepts and learn from the outcomes.

• They update the product plan based on findings from experiments and user tests.

6. Influencer

• The Product Owner builds strong ties with stakeholders and the development team. This helps gain support for the product vision.

•They articulate the vision of the product in a manner that inspires and excites others.

• They negotiate and collaborate with various groups to come up with solutions that work for all and are in line with the goals of the product.

Difference Between a Scrum Master and a Product Owner

The biggest difference between a Scrum Master and a Product Owner is how they collaborate with the team and the stakeholders (the individuals who are interested in the project).

•A Scrum Master is someone who is proficient in Agile approaches, which is a style of working that allows teams to make progress step by step. The Scrum Master ensures the team adheres to these approaches and communicates effectively.

•A Product Owner is the one who determines what features the product must have. They are responsible for ensuring the product fulfills customer requirements and remains in accordance with the business objectives

How a Product Owner Interacts with the Scrum Team

A Product Owner works with the Scrum team in many ways. This helps the team deliver a successful product.

1. Assisting in defining and elaborating on the product backlog:

The Product Owner collaborates with the team to determine what should be accomplished and in what priority.

2. Giving feedback during Sprint reviews:

The Product Owner checks the team's work during reviews. They also suggest improvements for the next steps.

3. Clarifying questions in Sprint planning and daily meetings:

The Product Owner is there to help explain questions and offer the team correct information.

4. Ensuring that the team understands the product vision and goals:

The Product Owner shares the bigger picture and keeps the team informed of what the product should be doing.

Why Does a Scrum Team Require a Product Owner?

A Product Owner has a vital role in leading the Scrum team:

1. Defining and prioritizing the product backlog:

The Product Owner determines what the team should do first and what's most valuable for the product.

2. Ensuring the team works on the most valuable features:

The Product Owner ensures the team works on features that customers will adore and that will make the business successful.

3. Serving as the contact point for stakeholders:

The Product Owner interfaces with stakeholders (such as customers or business managers) and ensures their requirements are included in the product.

4. Deciding and leading the team:

The Product Owner decides what is to be done and ensures the team remains on track to their objectives.

5. Keeping the product roadmap:

The Product Owner refines the product plan, ensuring it keeps pace with evolving customer requirements and shifts in the market

How Is a Product Owner Different from a Scrum Master or Project Manager?

There are three principal roles in Scrum, and each plays distinct responsibilities:

•  Product Owner:

The Product Owner is responsible for specifying what the product requires, prioritizing which features are most crucial, and ensuring the product is aligned with customer requirements and business objectives.

•  Scrum Master:

The Scrum Master ensures the team is using the Scrum process accurately. They assist in clearing away impediments and promoting collaboration to produce quality work.

•   Project Manager:

A Project Manager oversees the whole project, such as budget, timeline, and risks. They ensure the project is completed on schedule and in the budgeted cost.

How to obtain Big Data certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Become a Product Owner Today! To become a great Product Owner, you must understand the business and industry, comprehend customer needs, and be able to convert that into product requirements. A Product Owner plays a crucial role in providing quality products that satisfy customer requirements and contribute to the value of the company. If you want to enhance your skills, iCert Global provides Certified ScrumMaster® (CSM) and Certified Scrum Product Owner® (CSPO) courses, which can assist you in getting certified and advancing your career!

Contact Us For More Information:

Visit :www.icertglobal.com Email : 

Data engineering is growing quickly. It’s important to stay updated on new trends, tools, and techniques for success in this field. In 2025, demand for data engineers will increase. About 11,500 new jobs will open each year until 2031 Reading good books on data engineering helps everyone, whether they're experts or beginners. This guide has it all. You’ll find basic ideas and advanced skills to help you stay ahead in this changing field.

Best Data Engineering Books for 2025

Fundamentals of Data Engineering – Joe Reis (2022)

This book is a great starting point for learning data engineering. It explains key topics like data modeling, ETL (Extract, Transform, Load), data pipelines, and data warehouses. It also teaches how to design strong and reliable data systems. If you want a solid foundation in data engineering, this book is a must-read for 2025!

Designing Data-Intensive Applications – Martin Kleppmann (2017)

This book helps you understand how big data applications work. It covers important topics like data storage, distributed computing, and data processing. Using real-life examples, it teaches you how to build strong and scalable data systems. If you want to work with large amounts of data, this book is a great choice.

The Data Warehouse Toolkit – Ralph Kimball

Ralph Kimball’s book is a top resource for learning how to design data warehouses. It explains simple but powerful methods for organizing data so it can be easily analyzed. The book has real-world examples and case studies. This makes it helpful for beginners and seasoned data engineers in 2025.

Big Data: Principles and Best Practices of Scalable Realtime Data Systems – James Warren (2015)

This book explains how real-time data systems collect, store, and process information. It covers key topics like distributed computing, stream processing, and real-time analytics. James Warren covers the challenges of big data. He shares ways to build systems that are quick, dependable, and able to grow.

Spark: The Definitive Guide – Matei Zaharia (2018) Matei Zaharia’s book is an excellent guide to Apache Spark. It’s a key tool for managing big data.

It describes how Spark operates. It covers distributed computing, data processing, machine learning, and real-time analytics. This book uses clear explanations and real-world examples. It helps readers learn how to use Spark for various big data tasks. It’s a must-read for anyone looking to learn Spark and use it to manage large amounts of data efficiently.

Data Science for Business – Tom Fawcett (2013)

This book teaches how data science can help businesses make smart decisions. Tom Fawcett covers important topics like data mining, predictive modeling, and machine learning. He also shows how companies use data to stay ahead of competitors. This book uses simple examples to show readers how to use data. Readers can solve real-world business problems with these lessons. It's a valuable tool for anyone wanting to use data for smarter business decisions in 2024 and beyond.

Data Engineering with Python – Paul Crickard (2020)

Paul Crickard's book offers a practical approach to using Python in data engineering.It covers key topics like creating data models, building ETL (Extract, Transform, Load) pipelines, and automating data processing. The book goes beyond theory. It offers real examples and Python code. Readers can use these tools to create their own data solutions. It emphasizes scalability and efficiency. This makes it a useful resource for anyone learning to manage large datasets with Python.

Data Mesh – Zhamak Dehghani (2021)

This book introduces Data Mesh, a new way to manage data in big companies. It encourages giving teams control over their own data instead of having it all in one spot. This helps companies scale, organize, and use data more efficiently. The book discusses the challenges of using this system. It also shares real-world examples to help businesses switch. It’s a great read for data engineers and architects looking to modernize data systems in 2025.

Preparation Tips for Data Engineering

Getting ready for a data engineering job requires both technical skills and hands-on experience. Here are some tips to help you prepare:

1. Focus on these programming languages: Python, Java, Scala, and SQL. They are popular in data engineering. Practice writing clean, efficient code for handling and processing data.

2. Get Familiar with Data Technologies : Get to know popular tools like Apache Hadoop, Apache Spark, and Kafka. Also, look into various databases, such as SQL and NoSQL.

Understand how they work and how they fit into data pipelines.

3. Understand Data Modeling: Build a strong foundation in data modeling techniques such as dimensional modeling, entity-relationship modeling, and schema design. Organizing data properly makes it easier to analyze.

4. Work on Real Projects : Practice with real-world projects to gain hands-on experience. Try building data pipelines, writing ETL scripts, and working with data warehouses. You can also join online competitions to improve your skills.

5. Stay Updated : The world of data engineering changes fast. So, keep learning about new tools and techniques. Follow industry blogs, join online forums, attend webinars, and connect with other data engineers to stay ahead.

6. Improve Soft Skills : Besides technical skills, communication, problem-solving, and teamwork are important. Data engineers work with various teams. They need to explain technical ideas to non-technical people. This skill is very important.

Follow these steps to get ready for a successful career in data engineering

More Ways to Learn Data Engineering

• Online Courses and Tutorials Take courses online from iCert Global. They can help you boost your programming skills.
• These courses offer lessons on basic and advanced data engineering. You will learn with videos, do assignments, and tackle projects.
• Books and Reading Materials Read books and blogs by data engineering experts.

Some great books are:

• Designing Data-Intensive Applications by Martin Kleppmann
• Data Engineering Teams by Dave Holtz

1. Open Source Projects Join open-source projects on sites like GitHub. Working with other developers on real projects helps you gain experience. It also lets you demonstrate your skills to employers.
2. Competitions Compete in data challenges on platforms like Kaggle. These contests let you tackle real-world problems. You’ll work with big data and build teamwork skills.
3. Networking and Communities: Join online forums like LinkedIn. Connect with other data engineers. Ask questions, share ideas, and learn from others.
4. Bootcamps and Workshops Join bootcamps and workshops hosted by tech companies or universities. These programs give you hands-on training, expert mentorship, and networking opportunities.

How to obtain Big Data certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration

The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Data engineering is a smart career choice. It’s especially strong in technology, finance, healthcare, and e-commerce. Learning the right skills and getting real-world experience will help you succeed. One great way to build these skills is by joining the Post Graduate Program in Data Engineering. This course teaches everything from basic concepts to advanced techniques in big data. You'll tackle real-world projects and case studies. You'll also learn from experts about tools like Hadoop, Spark, and Kafka.

Contact Us For More Information:

Visit :www.icertglobal.com Email : 

Every business, whether small or large, needs useful information and insights. To understand customers and what they like, big data is very important. It even helps businesses predict what customers will want in the future. But having the right information isn’t enough—it needs to be shown clearly and analyzed properly. This helps businesses reach their goals. In this article, we will talk about how Big Data helps in:

• Talking to customers
• Improving products
• Identifying risks
• Keeping data safe
• Creating new ways to make money

What is Big Data in Business?

Big Data is a term for collecting and using huge amounts of information. Businesses wanted to understand patterns and trends. They saw this need came from the huge data created by how people use technology. Big Data helps companies understand customer behavior. They can then improve their services, products, and the overall experience.

Big Data – A Competitive Advantage

• Many successful companies use Big Data to stay ahead of their competitors. In almost every industry, businesses use data to grow, improve, and come up with new ideas.
• For example, in healthcare, experts use data to study how medicines work. They find risks and benefits that might not have been noticed during early tests. Other companies use sensors in products like machines and toys to see how people use them in real life. This helps them design better products and services for the future.
• Experts say that Big Data can bring many new opportunities for businesses to grow. It can even lead to new types of companies, like those that study and organize industry data. These companies gather and study a lot of data. They focus on products, services, buyers, sellers, and what customers prefer. Because of this, businesses in all industries should start using Big Data as soon as possible.
• Big Data matters not only for the volume of information but also for the speed of its collection. In the past, businesses had to look at customer loyalty after it had already happened. But now, with Big Data, they can study this in real-time and predict future trends. This helps businesses make better decisions quickly.
• Big Data is useful in many ways. It is already being used by both the government and private companies. In the next section, we will discuss some of the biggest benefits of Big Data in business.

Talking to Customers

Nowadays, customers are smart and know what they want. Before buying, they check out various options. They also chat with businesses on social media. Many customers also expect special treatment and like being appreciated for their purchases.

Big Data helps businesses understand their customers better. It allows companies to talk to customers in real-time and give them a personal experience. This is very important in today’s competitive world.

For example, imagine a customer walks into a bank. With Big Data, the bank clerk can quickly check the customer’s details and past activity. This helps the clerk suggest the right products and services that fit the customer’s needs.

Big Data also helps connect online and in-person shopping. For example, an online store can offer a special discount based on a customer’s social media activity.

Improving Products

Big Data helps businesses understand what customers think about their products. It collects feedback so companies can make improvements. By studying social media posts, businesses can learn what customers like or dislike. They can even see feedback from different locations and age groups.

Big Data also makes it easier to test new product designs quickly. Companies can check different materials, costs, and performance to make better products faster.

Understanding Risks

Success isn’t just about how a company runs—things like the economy and social trends also matter. Big Data helps businesses predict risks by analyzing news and social media. This keeps companies updated on important changes in the industry.

Keeping Data Safe

Big Data helps businesses keep their important information safe. It can spot security threats and safeguard sensitive data. This is crucial for banks and companies that deal with credit cards. Many industries use Big Data. This helps keep customer data safe and ensures it follows security rules.

Making More Money

Big Data doesn’t just help businesses—it can also create new ways to make money. Companies can sell non-personalized data about trends to other businesses. This helps industries improve their strategies and make smarter decisions.

Big Data is becoming more important in many industries. To make the most of it, businesses need to train their employees in data management. Icert Global offers a Post Graduate Program in Data Science. It helps professionals build skills in Big Data.

If you’re interested in a career in Big Data, check out the Big Data Career Guide. You’ll see the top skills needed, companies that are hiring, and a personalized plan to become a Big Data expert.

How to obtain Big Data certification? 

We are an Education Technology company providing certification training courses to accelerate careers of working professionals worldwide. We impart training through instructor-led classroom workshops, instructor-led live virtual training sessions, and self-paced e-learning courses.

We have successfully conducted training sessions in 108 countries across the globe and enabled thousands of working professionals to enhance the scope of their careers.

Our enterprise training portfolio includes in-demand and globally recognized certification training courses in Project Management, Quality Management, Business Analysis, IT Service Management, Agile and Scrum, Cyber Security, Data Science, and Emerging Technologies. Download our Enterprise Training Catalog from https://www.icertglobal.com/corporate-training-for-enterprises.php and https://www.icertglobal.com/index.php

Popular Courses include:

• Project Management: PMP, CAPM ,PMI RMP

• Quality Management: Six Sigma Black Belt ,Lean Six Sigma Green Belt, Lean Management, Minitab,CMMI

• Business Analysis: CBAP, CCBA, ECBA

• Agile Training: PMI-ACP , CSM , CSPO

• Scrum Training: CSM

• DevOps

• Program Management: PgMP

• Cloud Technology: Exin Cloud Computing

• Citrix Client Adminisration: Citrix Cloud Administration