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Azure Data Factory Mapping Data Flows provides a code-free design environment for building and operationalising ETL data transformations at scale. Now, the ADF team has added parameter support for Data Flows, enabling flexible & reusable data flows that can be called dynamically from pipelines.
Azure Data Factory upgraded the Teradata connector with new feature adds and enhancement, including built-in Teradata driver, out-of-box data partitioning to performantly ingest data from Teradata in parallel and more.
A new logging mode in Diagnostic Settings for an Azure Logs target, starting with Azure Data Factory, will allow you to take advantage of improved ingestion latency, query performance, data discoverability and more!
You can use ADF delete activity in your pipeline to delete undesired files without writing code.
Azure Data Factory now enables you to copy data from SAP Table and SAP Business Warehouse (BW) via Open Hub by using Copy activity.
Azure Data Factory seamlessly integrates with Polybase to empower you to ingest data into SQL DW performantly. ADF now adds support for loading data from ADLS Gen2 and from Blob with VNet service endpoint using PolyBase.
Azure Data Factory is now generally available in Azure China (China East 2).
Azure Data Factory copy activity now supports built-in data partitioning to performantly ingest data from Oracle database in parallel.
Azure Data Factory empowers you to copy data from Azure Data Lake Storage (ADLS) Gen1 to Gen2 easily and performantly. Furthermore, now you can choose to preserve the access control lists (ACLs) set on the files/directories along with data.
The new Mapping Data Flows feature in Azure Data Factory allows Data Engineers to visually design, debug, manage and operationalise data transformations at scale in the cloud.
You can use Azure Data Factory to operationalise 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 parameterise 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.
Organisations 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.