Table of Contents

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  1. Preface
  2. Introduction to Informatica Data Engineering Integration
  3. Mappings
  4. Mapping Optimization
  5. Sources
  6. Targets
  7. Transformations
  8. Python Transformation
  9. Data Preview
  10. Cluster Workflows
  11. Profiles
  12. Monitoring
  13. Hierarchical Data Processing
  14. Hierarchical Data Processing Configuration
  15. Hierarchical Data Processing with Schema Changes
  16. Intelligent Structure Models
  17. Blockchain
  18. Stateful Computing
  19. Appendix A: Connections Reference
  20. Appendix B: Data Type Reference
  21. Appendix C: Function Reference

PowerExchange Adapter Targets

PowerExchange Adapter Targets

PowerExchange targets can run in the Hadoop or Databricks environment, based on the adapter.
The following table lists PowerExchange targets and the non-native engines that support the targets:
PowerExchange Adapter
Supported Engines
Amazon Redshift
  • Blaze
  • Spark
  • Databricks Spark
Amazon S3
  • Blaze
  • Spark
  • Databricks Spark
Google Analytics
  • Spark
Google BigQuery
  • Spark
Google Cloud Spanner
  • Spark
HBase
  • Blaze
  • Spark
HDFS
  • Blaze
  • Spark
JDBC V2
  • Spark
  • Databricks Spark
Hive
  • Blaze
  • Spark
MapR-DB
  • Blaze
  • Spark
Microsoft Azure Blob Storage
  • Spark
  • Databricks Spark
Microsoft Azure Cosmos DB
  • Spark
  • Databricks Spark
Microsoft Azure Data Lake Storage Gen1
  • Spark
  • Databricks Spark
Microsoft Azure Data Lake Storage Gen2
  • Spark
  • Databricks Spark
Microsoft Azure SQL Data Warehouse
  • Spark
  • Databricks Spark
Snowflake
  • Spark
Teradata Parallel Transporter API
  • Blaze
  • Spark


Updated September 07, 2020