Top Technical Skills for Aspiring Big Data Engineers | iCert Global

A data engineer is an individual who designs and maintains the systems that store and process enormous amounts of data. They make sure that everything is working properly and smoothly.

Difference between Data Engineer and Big Data Engineer

We are living in the age of highly consequential data—like fuel for the contemporary world. During the past 20 years, many new technologies and data storage methods have appeared, like NoSQL databases and Big Data tools.

As Big Data became more prominent in handling information, the role of a Data Engineer evolved. Today, they need to handle a far more intricate and enormous array of data systems. Due to this, the role evolved into a new one — that of a Big Data Engineer.

Big Data Engineers need to learn the art of making use of specialized tools and databases to develop, construct, and maintain systems that can process huge data.

What does a Data Engineer do?

1. Gathering Data

Data ingestion is where we collect data from a multitude of sources and put it all in one location, such as a data lake. It can be very dissimilar sources—dissimilar formats and ways of storing data.

In Big Data, this is tougher because there is a lot more data, and it is of different types. Data Engineers also utilize technologies like data mining and data ingestion APIs to help collect and transfer all this data in the right manner.

2. Changing Data

Received raw data is not always usable immediately. It may be messy or in the wrong form. Data Engineers must deal with cleaning, transforming, or reorganizing the data into a form where it can be useful.

3. Performance Optimization

They ensure everything runs correctly and without problems. Data Engineers contribute towards making their systems efficient and quick.

Principal Job Responsibilities of a Big Data Engineer :

• Establish and maintain data pipelines (data paths data moves from source to storage)

• Gather and convert raw data from different sources for business use.

• Creating a data system that works efficiently and can expand is challenging.

• Utilize NoSQL databases and Big Data tools

• Design a way to gather, store, and transform large amounts of data for analysis.

Skills to be a Big Data Engineer

Big Data Technologies / Hadoop-Based Technologies

When Big Data became so important, engineers needed a better way to deal with it. That is when Doug Cutting developed Hadoop. Hadoop supports:

• Store Big Data in numerous computers

• Quickly process data with many systems simultaneously

• Important Big Data Tools and Technologies You Should Be Familiar With

HDFS (Hadoop Distributed File System)

HDFS is the data-storing component of Hadoop. It stores data on numerous computers, not on a single computer. Because HDFS is at the center of Hadoop, it's extremely important to learn about it before utilizing the Hadoop system.

YARN

YARN is utilized for resource management in Hadoop. It offers the proper amount of computer power to a job and provides timely work. YARN came into being in the second iteration of Hadoop. It made Hadoop robust, adaptable, and faster.

MapReduce

MapReduce is an approach to handle bulk data by dividing it into tiny pieces and processing them all at once. MapReduce handles data stored in HDFS in a direct manner.

Pig and Hive

• Hive is a program for checking data stored inside HDFS. Individuals familiar with SQL (a database language) are comfortable with Hive.

• Pig is a data transformation and cleaning scripting language. It is widely used by people who research and build software.

Both the applications help you manage huge data and are easy to learn if you have basic SQL knowledge.

Flume and Sqoop

• Flume imports unstructured data (e.g., logs and social media) into HDFS.

• Sqoop copies data that is organized, like from MySQL or Oracle, into HDFS and also back out.

ZooKeeper

ZooKeeper is similar to a team leader. It enables various services in a Hadoop ecosystem to talk to one another, organize, and work together.

Oozie

Oozie is similar to a planner of schedules. It links a series of little jobs in order to accomplish a huge task, e.g., a to-do list with steps.

Apache Spark (Real-time Processing)

All such systems as fraud detectors and recommenders must now handle data in real-time. Apache Spark enables real-time processing of data. It is Hadoop compatible and stores data in HDFS. All Data Engineers must be experts in real-time systems such as Spark.

Key Database Information for Data Engineers

Database Structure

Databases hold an enormous amount of information. Data Engineers must understand how databases are created. This means learning about:

• 1-tier, 2-tier, 3-tier, and multi-tier systems

• Data models (data organization)

• Schemas for data (the database blueprint or structure)

SQL-Based Technologies (Like MySQL)

SQL is a programming language used to control and organize data in databases. SQL must be known and implemented by Data Engineers.

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

All data is not clean and organized. NoSQL databases are appropriate to store all kinds of data—structured, semi-structured, and unstructured. Popular Databases and Tools That Every Data Engineer Must Learn

HBase

HBase is a NoSQL database with column-family storage. It is implemented on top of HDFS and is appropriate for large systems where data is to be read quickly or searched.

Cassandra

Cassandra is yet another NoSQL database that can scale without much effort as your data grow. It can also process lots of read and write operations in parallel and will continue to run even when one of its pieces of hardware fails.

MongoDB

MongoDB is a NoSQL database in which data is kept as documents rather than tables. It does not have a required structure, so you can modify the manner in which you store data as your application grows. It enables you to perform quick searches and copy data across systems to protect it.

Programming Languages: Python and R

• To be a Data Engineer, you need to know one programming languege well

• It is simple to learn due to its simple rules and extensive community support. It is an excellent option for beginners.

• R is harder to master and is mainly utilized by statisticians, analysts, and data scientists for sophisticated data analysis.

ETL and Data Warehousing Tools (Such as Talend and Informatica)

When an organization receives data from numerous sources, it needs to collect, clean, and store such data. This activity is referred to as ETL (Extract, Transform, Load). The data is then stored in a Data Warehouse, and it is used for analysis as well as for reports.

Big data engineer tools, systems, and salary data.

Informatica and Talend

Informatica and Talend are both popular data movement and management software packages. They employ the ETL approach—Extract, Transform, Load, i.e., they assist in data retrieval, cleansing, and storage in storage systems.

Talend Open Studio is extremely helpful as it is compatible with Big Data tools. As a beginner, it is normal to begin with Talend.

Why to become a Data Engineer?

One major reason individuals desire to be Data Engineers is that they are well paid. They also desire to know how data flows and how companies utilize it.

How to obtain  Big Data certification? 

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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

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• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

Conclusion

Big Data Engineers play an exceptionally vital role of managing big data with the assistance of strong tools and technologies. It is necessary to learn skills such as Hadoop, SQL, NoSQL, and real-time processing in order to do well in this role. With proper knowledge, Big Data Engineers are able to design efficient systems and enjoy good employment opportunities.

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