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Azure Data Factory adds new features for ADF pipelines, Synapse pipelines and data flow formats
Azure Data Factory adds new features for data flows to handle hierarchical data mapping and complex joins and lookups
Target availability: Q2 2020
Update your servers/machines running self-hosted integration runtimes that communicate with the Azure Data Factory backend for control plane actions to use transport layer security (TLS) 1.2 by May 11, 2020.
Azure Data Factory adds Managed Identity & Service Principal to Data Flows Synapse staging for fast, secure data loading.
Data Factory adds Flatten transformation, single-row lookups, and updated UI for container activities
Azure Data Factory added several new features to mapping data flows and pipeline activities this week: Flatten transformation, Lookup transformation, container UI.
Azure Data Factory added several new features to mapping data flows this week: Import schema and test connection from debug cluster, custom sink ordering.
Azure Data Factory users can now build Mapping Data Flows utilized Managed Identity (formerly MSI) for Azure Data Lake Store Gen 2, Azure SQL Database, and Azure Synapse Analytics (formerly SQL DW).
Azure Data Factory is now available in the South Africa North region.
Azure Synapse Analytics introduced a new COPY statement (preview) which provides the most flexibility for high-throughput data ingestion.
Azure Data Factory copy activity now supports resume from last failed run when you copy files between file-based data stores to ease large volumes of data ingestion and migration.
Azure Data Factory now supports SFTP as a sink and as a source. Use copy activity to copy data from any supported data store to your SFTP server located on-premises or in the cloud.
Azure Data Factory expands parameterization capabilities to make data flows even more reusable and scalable.
Azure Data Factory copy activity now supports preserving metadata during file copy among Amazon S3, Azure Blob, and Azure Data Lake Storage Gen2.
Azure Data Factory wrangling data flows are now in preview.
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.
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