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You now have the ability to run your Azure Machine Learning service pipelines as a step in your Azure Data Factory pipelines. This allows you to run your machine learning models with data from multiple sources (more than 85 data connectors supported in Data Factory).
Mapping Data Flows feature is now generally available in Azure Data Factory
Azure Data Factory now supports Azure Database for PostgreSQL as a sink. Use the copy activity feature to load data into Azure Database for PostgreSQL from any supported data source.
Load data faster with new support from the Copy Activity feature in Azure Data Factory. Now, if you’re trying to copy data from any supported source into SQL database/data warehouse and find that the destination table doesn’t exist, Copy Activity will create it automatically.
Azure Data Factory now supports copying data into Azure Database for MySQL. Use the Copy Activity feature to load data into Azure Database for MySQL from any supported data sources.
Azure Data Factory now provides built-in data partitioning to copy data from Netezza.
Azure Data Factory has added the ability to execute custom SQL scripts from your SQL sink transformation in mapping data flows. Now you can easily perform options such as disabling indexes, allowing identity inserts and other DDL/DML operations from data flows.
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.
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.
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