Table of Contents

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  1. Preface
  2. Introduction to Data Engineering Streaming
  3. Data Engineering Streaming Administration
  4. Sources in a Streaming Mapping
  5. Targets in a Streaming Mapping
  6. Streaming Mappings
  7. Window Transformation
  8. Appendix A: Connections
  9. Appendix B: Monitoring REST API Reference
  10. Appendix C: Sample Files

Targets in a Streaming Mapping on Databricks

Targets in a Streaming Mapping on Databricks

A streaming mapping that runs in the Databricks environment can include file and streaming targets.
Based on the type of target you write to, you can create the following data objects:
Azure Event Hubs
A physical data object that represents data in Microsoft Azure Event Hubs data streaming platform and event ingestion service. Create an Azure Even Hub data object to connect to an Event Hub target.
Microsoft Azure Data Lake Storage Gen2
A Microsoft Azure Data Lake Storage Gen2 data object is a physical data object that represents a Microsoft Azure Data Lake Storage Gen2 table. Create a Microsoft Azure Data Lake Storage Gen2 data object to write to a Microsoft Azure Data Lake Storage Gen2 table.
Databricks Delta Lake
A Databricks Delta Lake is an open source storage layer that provides ACID transactions and works on top of existing data lakes. Create a relational data object to write to a Databricks Delta Lake target. To configure the connection, see JDBC Connection Properties.
Effective in version 10.4.0, Databricks Delta Lake is available for technical preview.
Technical preview functionality is supported for evaluation purposes but is unwarranted and is not production-ready. Informatica recommends that you use in non-production environments only. Informatica intends to include the preview functionality in an upcoming release for production use, but might choose not to in accordance with changing market or technical circumstances. For more information, contact Informatica Global Customer Support.