Companies have thousands of data processing jobs running in their data centers, also known as ETL processes. These ETL processes read data from external or internal data sources, transform the data, and then load in a database or a data lake. With the advent of Big Data and cloud, more and more ETL processes are developed using Spark and Map-Reduce.
As the number of processes and data volume has increased exponentially, manual testing is almost impossible. Organizations are extensively looking DevOps, CI/CD and QA automation solutions to test and certify these ETL processes.