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

Databricks and Delta Lake Tables

Databricks and Delta Lake Tables

Mappings can access managed and unmanaged Databricks tables.
Delta Lake is an open-source data lake resource that stores data in large tables. Databricks manages stored data and allows fast access to the data. Delta Lake supports ACID transactions and scalable handling of metadata. You can build a lakehouse with Databricks Delta Lake using compute engines such as Spark, PrestoDB, Flink, Trino, and Hive along with APIs for Scala, Java, Rust, and Python.
Mappings can also access unmanaged Databricks tables built on top of any of the following storage types:
  • Azure blob storage
  • Azure Data Lake Storage (ADLS) Gen1 or Gen2
  • Amazon Web Services (AWS) S3
For more information, see the Databricks documentation.
You can configure dynamic mappings to apply Databricks source schema changes to Delta Lake targets. See Databricks Schema Evolution.
To use Delta Lake resources in a mapping, create a relational data object and use a JDBC connection to connect to the resource.


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