Mapping Data Flows in Azure Data Factory hits general availability

Earlier this year, in May, Microsoft introduced a number of new features for its Azure Analytics and database space. As part of these, Azure Data Factory (ADF) also got some new capabilities. For those unaware, ADF is a hybrid data integration service enabling simplification of large-scale extract/transform/load (ETL). Among the capabilities it received was the Mapping Data Flows feature.

Now, Microsoft has announced that Mapping Data Flows has hit general availability (GA). With this, the company is offering faster, easier, and more accessible data integration and transformation for organizations.

Mapping Data Flows allows users to build data pipelines in an accessible visual environment, without having to go through the additional hassle of infrastructure management. With this, ADF provides an environment that enables data scaling without limitations, along with simplified ETL, with minimal coding knowledge required. Data processing is also made much easier, with the availability of in-built capabilities to handle unpredictable data schemas.

ETL tasks such as loading fact tables, maintaining slowly changing dimensions, aggregating semi-structured big data, and more can be performed efficiently. Particular focus is laid upon analytics in business logic, with an intuitive visual interface that helps transform raw data into insights. The already provided transformations can also be customized through the expression builder. And finally, the drag-and-drop pipeline builder enables end-to-end debugging for ETL processes, making it much easier to monitor data flow executions.

You can learn more about Mapping Data Flows through its GitHub documentation, while those who want to jump right can do so by following the instructions provided here.

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