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Data Factory Mapping Data Flows has added a new "Quick Actions" feature to automate the creation of data transformations.
Gantt views are now available for monitoring data factory pipelines.
Create dependent pipelines in your Azure Data Factories by adding dependencies among tumbling window triggers in your pipelines.
New features added to the ADF service this week make handling flexible schemas and schema drift scenarios super easy when construction Mapping Data Flows for data transformation at scale
You can use ADF delete activity in your pipeline to deletes undesired files without writing code.
You can use Azure Data Factory to operationalize your Azure HDInsight Spark and Hadoop workloads against HDInsight clusters with Enterprise Security Package that are joined to an Active Directory domain.
You can monitor the health of your data factory pipelines by using the Azure Data Factory Analytics service pack available in the Azure Marketplace.
Azure Data Factory visual tools are now integrated with GitHub, so you can collaborate with other developers, do source control, and version your data factory assets.
You can share an existing self-hosted IR with multiple data factories. This functionality helps you reuse an existing self-hosted IR infrastructure in another data factory while building a hybrid data-integration pipeline.
You can now parameterize a linked service in Azure Data Factory.
Azure Data Factory now generates linked Resource Manager templates for CI/CD by breaking the factory payload into several files to help users with Resource Manager template limits.
Azure Functions is now integrated with Azure Data Factory, so you can run an Azure function as a step in your data factory pipelines.
Organizations can now improve operational productivity by creating alerts on data integration events (success/failure) and proactively monitor with Azure Data Factory.
Azure Data Factory pipelines now include tagging support and enhanced monitoring capabilities, including dashboards and improved debugging support.
You can visually design, build, and manage data transformation processes without learning Spark or having a deep understanding of the distributed infrastructure.
Data Engineers or data developers can now use templates in Azure Data Factory to quickly build data integration pipelines, improve developer productivity, and reduce development time for repeat processes.