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What is Data Engineering? How to Become a Data Engineer?

Posted in Data Science
What is Data Engineering? How to Become a Data Engineer?


As the world is going gaga about data science and data scientists, there is another related field that is picking up a lot of attention – data engineering. A data engineer plays an important role in managing ETL processes, workflows, pipelines, and much more. Data engineers are increasingly in demand and are offered attractive packages because of the exciting and challenging work that they get every day. You can think of a data engineer’s job to be somewhere between a data analyst and a data scientist.

What is Data Engineering?

Data engineering is a valuable field of learning which deals with processing, storage, and delivery of vast amounts of data. If data analysis is about modeling the data, data science is about making decisions; data engineering helps enable these two fields by providing the necessary infrastructure. A data engineer can build or design pipelines to transport, store, and transform data. The pipelines take data from various sources and store them into a single warehouse.

The final role of a data engineer is to provide a robust and reliable infrastructure to support big data.

Where Does Data Engineering Fit in the Data Science Lifecycle?

Data engineering consists of the following steps:

  • Data Collection: Data can be collected using various sources – logs, databases, external sources, user-generated content, sensors, instrumentation, etc.
  • Movement and Storage of Data: this involves data flow, pipelines, storage of structured and unstructured data, ETL, infrastructure.
  • Data Preparation: cleaning and processing to remove anomalies in data

These are important steps for data preprocessing before performing analysis. Only after the data is prepared, it is sent for further cleaning, processing, transformation, analysis, and modelling.

Why Data Engineering?

Earlier, a data scientist or analyst would have to write big SQL queries and use various tools to perform ETL. However, with the popularity of big data, the roles of data analysts, data engineers, and data scientists have further narrowed down.

A data engineer has expertise in the following:

  • Adept in Python and SQL.
  • Experience in working with cloud services like AWS.
  • Working knowledge of Java or Scala.
  • Understand the differences between SQL and NoSQL databases and work with both.
  • Knowledge of ETL tools like Informatica PowerCenter, Oracle Data Integrator, AWS Glue, etc.

Since the responsibilities of a data engineer are clearly defined, a data scientist is free from learning technical details and can focus more on the business aspects of the problem in hand. Most data engineers are technically sound and have at least 4 out of the five skills mentioned above.

Essential Skills Required to Be a Data Engineer

Some companies do expect more from a data engineer – like the tech-giants Amazon, Facebook, Google – expect the below skills other than what we already mentioned:

  • Experience with big data – Spark/Hadoop/Kafka.
  • Basic knowledge of data structures and distributed systems.
  • Understanding algorithms.
  • Knowledge of visualization tools like Excel, Tableau, or any other.

This is because these companies have massive data to be stored (big data) and to process the same; the above skills are necessary. In the case of smaller companies, big data is not required, and it is sufficient to store the data in a central repository or “warehouse.”

Besides this, senior data engineers are expected to have some business intelligence experience and working knowledge of creating reports and dashboards.

Even with this clear distinction, there are still some skills that overlap for the roles of data scientists and data engineers. Since a data engineer is a technical person, he can perform the task of a data scientist as well; however, vice-versa may not be entirely possible.

How to Become a Data Engineer?

Now that we know “what is data engineering,” we will also guide you on how you can become a data engineer. You can do so partly by self-learning and partly by taking up the right courses for the same. If you have basic computer science knowledge and mathematics background, things might be more natural for you. Well, even if you aren’t from math background and don’t know how to write code, you can become a data engineer with a little extra effort. Remember the two keywords for becoming a data engineer – ‘computer science’ and ‘data’!

The tools and technical knowledge you need depends on the industry. For example, for financial services, SQL, Sybase, Oracle, C++ and Java are more popular. In contrast, in the consulting industry, which has a broader base, uses more modern tools like Hadoop, Spark, Java/Scala, cloud platforms like AWS, Azure, or Google.

Here is all the technical knowledge you need to start your career as a data engineer:

1. Data Structures and Algorithms

Data structures are a set of methods that enable the organization and storage of data for easy access and manipulation of data. Some examples of data structures are ArrayList, LinkedList, queue, maps, trees, etc. Algorithms are the set of code written to solve a problem. Algorithms use data structures for faster data processing and problem-solving. Check out the list of cool data structures and algorithms tutorials on hackr.io.

2. SQL

SQL is the most critical skill a data engineer should possess. Since the title itself has data, and SQL is entirely related to data, learning SQL will enable you to play with data and understand data in a better manner. If you know how to write queries, you can fetch any kind of data from the database in minutes. As a data engineer, you should be able to create database schemas, tables, and perform operations like grouping, sorting, joins, ordering, and other data manipulations. SQL forms an essential step in preparing data for further analysis. Learn SQL through any of the tutorials referred by hackr.io or if you already know you can brush up with the SQL cheat sheet.

3. Python, Java

Java and Python are the two most popular languages used for data science. Python is popular for its rich set of libraries that can perform almost every statistical and mathematical operation, and apply various algorithms, without much need for actual coding. Python is also easy to learn and read. You can start learning Python through these free and paid tutorials.

Java is essential for big data processing. The Map-Reduce algorithm in Apache Hadoop uses Java. Java is easy; however, not as easy as Python. If you have some programming background, you can pick up Java easily, however, if you are new to programming, start with Python and then move to Java. Here are Java tutorials that will help you!

4. Big Data

Big data can store humongous amounts of data, both structured, semi-structured, and unstructured. The demand for data has increased due to data science and AI; hence big data tools and techniques have become more critical than ever. Learning big data will help you understand how data is stored, processed, cleaned, and information is extracted from the huge datasets. Big data works on three main concepts: Volume, Velocity, and Variety.

Many data processing frameworks help process huge datasets in no time and perform distributed computing either on their own or with other tools. Some of the popular frameworks are Apache Spark, Hadoop, and Apache Kafka. Checkout tutorials listed by Hackr.io for these 3:

5. Cloud Platforms

Through cloud systems, resources can be made available on-demand. Resources can be accessed as service by any user over the internet. Businesses can now worry about their core business use cases and issues, and not bother about infrastructure or other IT related issues. Cloud systems are cheap and can be easily maintained. A cloud client can be a web browser, a mobile app, terminal, etc. There are three types of services provided by a cloud platform:

  • SaaS (Software as a Service): example, email, games, CRM, virtual desktop, etc.
  • PaaS (Platform as a Service): example, database, a webserver
  • IaaS (Infrastructure as a Service): e.g., servers, storage, virtual machines, network, etc.

The three major cloud providers are Google, Microsoft, and Amazon, and Hackr.io has consolidated all the right tutorials in one place so that you can choose the ones for you based on your experience level.

6. Distributed Systems

Distributed systems are a group of computers that work together but appear as a single computer for the end-user. Each computer is independent of the working of the other, and if one fails, the others are not impacted. Distributed systems allow for horizontal scaling, which enhances the overall performance and fault tolerance of the application. Learn more about distributed systems through this freecodecamp article.

7. Data Pipelines

Cloud platforms like AWS offer data pipelines, which is the core of data engineering. AWS data pipeline is simply a web service that can be used to automate the transformation and movement of data. A pipeline can schedule daily and weekly tasks and run them by creating separate instances. Learn about the AWS data pipeline.


Data engineers have a vast scope in the future and will be in high demand as long as the world is undergoing digital transformation. It is an essential step in the entire data science process and mostly technical all though you can significantly simplify the entire process using various tools. While data engineering may sound an easy bet initially, it involves a lot of challenges as most real-time data is unstructured and needs a lot of processing to be done. You can master data engineering with practice and knowledge of as many tools, algorithms, and data structures as possible. As you go to senior levels, you should be able to catch some aspects of AI too, which are essential for data engineering. If your final goal is to become a data scientist, data engineering could be your first and most essential step.

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Ramya Shankar

Ramya Shankar

A cheerful, full of life and vibrant person, I hold a lot of dreams that I want to fulfill on my own. My passion for writing started with small diary entries and travel blogs, after which I have moved on to writing well-researched technical content. I find it fascinating to blend thoughts and research and shape them into something beautiful through my writing. View all posts by the Author

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