Table of Contents


  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

Targets on Databricks

Targets on Databricks

A mapping that runs in the Databricks environment can include file and database targets.
The following table lists the storage types and targets:
Storage Type
  • Amazon Simple Storage Service (Amazon S3)
  • Databricks Delta Lake storage
  • Microsoft Azure Blob Storage
  • Microsoft Azure Data Lake Store (ADLS)
  • Amazon Redshift
  • JDBC V2
  • Microsoft Azure Cosmos DB
  • Microsoft Azure SQL Data Warehouse
  • Snowflake


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