Why do we need Azure Data Factory?

  • The amount of data created nowadays is enormous, and it comes from a variety of sources. When it comes to moving this data to the cloud, there are a few factors to consider.

  • Data may take any shape since it originates from a variety of sources, each of which will transmit or channelize the data in a different method and in a different format. When we move this data to the cloud or a specific storage location, we must ensure that it is effectively maintained. To put it another way, you’ll need to modify the data and remove any superfluous information. When it comes to transferring data, we must ensure that data is collected from many sources and brought to a common location, where it is then stored and, if necessary, transformed into something more useful.

  • This can also be done with a typical data warehouse, but there are certain drawbacks. We are often compelled to develop bespoke apps to handle each of these procedures separately, which is time-intensive and difficult to integrate all of these sources. We need to figure out a method to automate or develop suitable processes for this process.

  • The data factory facilitates the orchestration of this entire process in a more comprehensible or organized manner.

Azure Data Factory is a Microsoft cloud service offered by the Azure platform that allows the integration of data from many different sources. Azure Data Factory is a perfect solution when you need to create hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration pipelines. Provides access to local data in SQL Server and data in the cloud in Azure Storage (Blob and tables) and Azure SQL Database.

It allows you:

  • Copy data from many compatible sources, on-premises and in the cloud
  • Transform the data (see paragraphs below)
  • Publish the copied and transformed data, sending it to a destination data storage or analysis engine.
  • Monitor data flow using a rich graphical interface

Data Factory is not SQL Server Integration Services (SSIS) in the cloud. It has fewer database-specific features and focuses on supporting larger data movements and movements (including large data sets, including Data Lake operations).

However, Data Factory can run its SSIS packages in the cloud (once integrated with SSIS). This allows us to take advantage of the scalability of Data Factory with the advanced ETL functionality of SSIS.