Breaking Down Big Data Issues,Insights and Case Study | iCert Global

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? 

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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.

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