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.
"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.
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.
Data engineers typically do the following tasks:
Data engineers should have the below mentioned skills.
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
Snowflake Data Engineer
Data Engineering Consultant
Data Scientist, Data Engineer and Cloud Practitioner