With growing needs of analysing vast scales of data, Big Data Engineering services have emerged as a separate stream along with Data Science. While Data Science deals with generating insights from data, Data Engineering deals with managing and preparing the data for business analysis. Data Engineering was originally limited to data management on traditional data platforms like RDBMS, Datawarehouses (DW), Datamarts, etc,. Data Architects designed and managed the data models, data governance, master data management and data security.

ETL Engineers used to manage data pipelines and Data Analysts mostly generated reports using basic SQL and reporting tools. The statisticians ran models on the traditional data sets. In the last decade or so, the growth of data volume has become exponential in most of the industries. Traditional ETLs, Databases and statistical models couldn’t handle the volumes. Along with volume, there is an increasing need for analytics on variety of data, real-time data and quality of data. This triggered the need for Big Data platforms and Data Engineering for Big Data.