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Created Service Type Note Context Reference
10/14/2019 Data Factory New Features 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). The seamless integration enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns. Link Link Details
10/8/2019 Data Factory General Availability The new Mapping Data Flows feature in Azure Data Factory allows Data Engineers to visually design, debug, manage, and operationalize data transformations at scale in the cloud. With the addition of Mapping Data Flows, ADF becomes the Data Engineer’s one-stop service for scale-out ETL in the cloud. Link Link Details
9/20/2019 Data Factory New Features Load data faster with new support from the Copy Activity feature of Azure Data Factory. Now, if you’re trying to copy data from an on-premises source into Azure and find that the destination table doesn’t exist, Copy Activity will create it automatically. After the data ingestion, review and adjust the sink table schema as needed. Link Link Details
9/10/2019 Data Factory New Features You can now use the Azure Data Factory copy activity to ingest data from Netezza with out-of-box parallel copy to boost performance. With Netezza data slice partition and dynamic range partition support, Data Factory can run parallel queries against your Netezza source to load data by partitions concurrently, increasing performance. Link Link Details
8/28/2019 Data Factory New Features Data Factory has added a new capability that allows you to execute custom SQL scripts from your SQL Sink transformation in Mapping Data Flows. Now you can easily perform options such as disabling indexes, allows identity inserts, and other DDL/DML operations from Data Flows. Link Link Details
8/5/2019 Data Factory New Features Your complex data integration projects may have dependencies, which makes them an important aspect in job scheduling. Now, it’s possible to create dependent pipelines in your Azure Data Factories by adding dependencies among tumbling window triggers in your pipelines. By creating a dependency, you’re able to guarantee that a trigger is executed only after the successful execution of a dependent trigger in your data factory. Link Link Details
8/5/2019 Data Factory New Features Gantt views are now available for monitoring data factory pipelines. Use them to quickly visualize your data factory pipelines and activity runs. See the Gantt view per pipeline or group by annotations or tags that you created on your pipelines. Link Link Details
7/5/2019 Data Factory Preview Features The Azure Data Factory team has added parameter support to the Mapping Data Flows public preview feature that will now allow you to build configurable data transformation logic in a code-free design environment. So, if your requirements involve logic that is based on frequently changing attributes like time, date, location, price, cost, etc., it is easy to design transformation logic once and parameterize those values inside of your Data Flows. Link Link Details
7/5/2019 Data Factory Updated Features Azure Data Factory upgraded the Teradata connector with new feature adds and enhancement. More specifically: The Teradata connector is now empowered by a built-in driver, which save you from installing the driver manually to get started. You can now use copy activity to ingest data from Teradata with out-of-box parallel copy to boost performance. With hash partition and dynamic range partition support, data factory can run parallel queries against your Teradata source to load data by partitions concurrently to achieve better performance. It also addressed the issues like connection and query timeout that some customers hit earlier. Link Link Details
7/1/2019 Data Factory New Features Azure Data Factory logs now available as dedicated tables in Azure Monitor Logs. Link Link Details

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