Data Engineering Explained

The big thing that is happening in the current IT industry after Data Science is Data Engineering. As per Bureau of Labor statistics, it is forecasted that the Data Engineering field grows at a staggering 22% in this decade beating every other occupation. 

A lot of aspirants want to know what is Data Engineering? What are the roles and responsibilities of Data Engineers? And how is it different from Data Science? 

In this article, let us explore Data Engineering in depth. Let’s get started.

What is Data Engineering?

"You can have data without information, but you cannot have information without data.”  — Daniel Keys Moran

Data is growing rapidly. The growth seen in the companies utilizing the data efficiently is unparalleled. Any company which makes use of the growing data for decision making has an upper hand over traditional companies which relies solely on operations. Hence, every company want to make use of utilizing the data at hand for informed decision making. 

Be it for cost cutting or sales growth, optimized operations or capturing untapped markets, data is everywhere. This data must be collected, stored and processed before analyzing and arriving at decision making. This is where the Data Engineers come into play. 

Data Engineering is a field of designing software solution that can collect, store and transform the data from multiple sources and different formats. Their primary responsibility is to build, manage and optimize data pipelines and move these data pipelines into production. They act as a bridge between database administrators and data scientists.

Data Engineering vs Data Science

The line that separates Data Engineering and Data Science is getting masked day by day. More often than not, the data scientists work as data engineers and data engineers work as data scientists.

For clear understanding, data engineers collect, store and convert the raw data into a format ready to be consumed by the data scientists. Data scientist use this data for analyzing (or prediction) and decision making. If the core of data science is making future predictions by analyzing past data, data engineering is all about transforming the data and make it ready for end users (or data scientists) for consumption. 

The tools and technologies used by both data engineers and data scientists overlap by a higher degree. Data engineers differ from Data Scientist in that the data Engineers need not have experience in Statistics, Machine Learning/Deep Learning or Artificial Intelligence.

Roles and Responsibilities of Data Engineers

Data engineers typically do the following tasks:

  1. Acquire the data from multiple sources. Sometimes the data is generated internally like the customer billing data in the retail shops etc., sometimes the data is fetched from external sources like census etc. 
  2. Sometimes data acquisition may also require the software engineers to scrap the websites.
  3. Merge all the data acquired from multiple sources into a single source. A typical challenge the data engineers face is that the same data is acquired in different formats like currency expressed in INR vs USD, date format of dd-mm-yy vs YYYY/dd/mm etc.
  4. Cleaning the data is perhaps the biggest and most time-consuming task among all the tasks. As the data is collected from multiple sources, it is expected that the data needs to be cleaned. Usual scenarios are missing fields, missing data used to uniquely identify each record, errors like negative age, non-ascii characters in some fields, translation of other languages etc.
  5. Store the data in database or data warehouse. Common practice is by following 3NF (3rd Normal Form) along with indexing and partitioning of data.
  6. De-duplication of records and store the ‘single source of truth’ in a master table.
  7. Architect scalable end to end data pipelines.
  8. Getting the data ready for consumption. This can be a dashboard for end users or exposing it as an API etc.
  9. Lastly, all the above steps need to be performed on a daily, weekly or monthly basis. Hence, all the above steps must be automated and scheduled to run at a defined interval.

Skillset required

Data engineers should have the below mentioned skills.

  1. Programming language (must have): Any programming language is a must for data engineers. The current trend requires data engineers to learn Python, Spark or Hadoop.
  2. Database (must have): Experience in database is a must. Knowledge in SQL is desired.
  3. Cloud (good to have): Experience in any cloud platform (AWS, GCP, Azure etc.) is desired. As more and more companies are moving to cloud, very soon, experience in this arena becomes a must have.
  4. ETL tools (good to have): Learning or having work experience in ETL tools like Informatica is an added advantage.
  5. BI tools (good to have): Experience in BI tools like Tableau, Power BI is a great advantage.
  6. Pipeline tools (good to have): Cloud native platforms (Kubernetes) and DevOps experience is an added advantage.
  7. Other tools: Knowledge in GIT, Linux/Unix is a huge plus.


Though Data Engineering is a broad term, companies are preferring niche skills. As an aspirant, you can pick any one of the below career paths to become a data engineer.

  • Data Engineer using Python/PySpark

  • AWS/GCP/Azure Data Engineer

  • Bigdata Developer

  • Cloud Engineer/Architect

  • Snowflake Data Engineer

  • Data Engineering Consultant

Phani Kumar
Data Scientist, Data Engineer and Cloud Practitioner

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