For Data Engineering workloads within Microsoft landscape, there are multiple options to carry out Data Engineering tasks to extract data from myriad of data sources. Currently three options are available:
- SQL Server Integration Services (SSIS): It is part of Microsoft SQL Server Suite and SSIS is a very well-known popular ETL tool for Data Integration along with rich built in transformations. Introduced in 2005. Mainly for on-premises. Now you can run on-premise as well. Aggregations, splits and joins.
- Azure Data Factory (ADF): Unlike SSIS, ADF is a ELT tool along with Data Orchestration tool to build pipelines to move data across different layers. From on-Premise to Cloud and within Cloud landscape. Movement and Orchestration but not Transformations.
- Data movement & Orchestration
- Extract, Load & Transform
- Transformation activities.
People familiar with SSIS can use it and existing SSIS packages can also be migrated.
Azure Data Bricks: Azure Data Bricks is latest entry into this for Data engineering and Data Science workloads, unlike SSIS and ADF which are more of Extract Transform Load (ETL), Extract Load Transform (ELT) and data Orchestration tools, Azure data bricks can handle data Engineering and data science workloads.
In a nutshell, although you can compare and contrast these tools, they actually compliment each other. For example you can call existing SSIS packages using Azure Data Factory and trigger Azure data bricks notebooks using Azure Data Factory.
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