Even though the above diagram is a bit of simplification, this is how most ETL workflows may look like. To put simply, ETL is an automated process to move data from source systems to target systems, involving various stages for Extract, Transform and Load sub-processes, without data-loss and while maintaining data-integrity. This also, is usually referred to as data-migration. The objective of ETL is to have a clean, classified, enriched and curated data at one place (data warehouse or data lake). Machine-learning models and analytic tools are run against this data to fetch useful information and predictions, based on which business decisions can be taken.

Even though the above diagram is a bit of simplification, this is how most ETL workflows may look like. To put it simply, ETL is an automated process to move data from source systems to target systems, involving various stages for Extract, Transform and Load sub-processes, without data-loss and while maintaining data-integrity. This also, is usually referred to as data-migration. The objective of ETL is to have a clean, classified, enriched and curated data at one place (data warehouse or data lake). Machine Learning models and analytic tools are run against this data to fetch useful information and predictions, based on which business decisions can be taken.