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Table of Contents

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  1. Preface
  2. Release Notes for October 2025
  3. Administrator
  4. API Center
  5. API Manager
  6. API Portal
  7. Application Integration
  8. B2B Gateway
  9. B2B Partners Portal
  10. Business 360 Console
  11. CLAIRE GPT
  12. Cloud Data Integration for PowerCenter (CDI-PC)
  13. Data Governance and Catalog
  14. Data Ingestion and Replication
  15. Data Integration
  16. Data Marketplace
  17. Data Profiling
  18. Data Quality
  19. Data Validation
  20. Human Tasks
  21. Integration Hub
  22. MDM - Customer 360 SaaS
  23. MDM - Product 360 SaaS
  24. MDM - Reference 360
  25. MDM - Supplier 360 SaaS
  26. Metadata Command Center
  27. Monitor
  28. Multidomain MDM SaaS
  29. Operational Insights

Release Notes

Release Notes

Databricks Connector

Databricks Connector

Fixed issues

The following table describes fixed issues:
Issue
Description
CCON-106328
When you run a mapping enabled for SQL ELT optimization to write multi-line string fields to a flat file and the data is enclosed in double quotes, the data in incorrectly written to the target. (October 2025)
CCON-105920
When you run a Databricks mapping and use the Permanent IAM credentials authentication to stage data in Amazon S3, the mapping fails with the following error if the temporary security credentials for Amazon S3 expire:
[ERROR] com.amazonaws.services.s3.model.AmazonS3Exception: The provided token has expired. Service: Amazon S3; Status Code: 400; Error Code: ExpiredToken;
(October 2025)

Known issues

The following table describes known issues:
Issue
Description
CCI-5192
When you run a mapping in advanced mode to write data to multiple Databricks targets and view the job results, the Individual Task Results section doesn't display results for each target separately. Instead, it displays a single consolidated result for all targets. However, the data is correctly written to all targets. (April 2025)

Third-party known issues

The following table describes third-party known issues:
Issue
Description
CCON-109034
When you stage data in a Volume in a mapping that writes to a Databricks target, the mapping might fail with the following error or result in partial data loss in the target table:
Error running query: [PATH_NOT_FOUND] org.apache.spark.sql.AnalysisException: [PATH_NOT_FOUND] Path does not exist: dbfs:/<VolumePath>. SQLSTATE: 42K03
(October 2025)
Databricks ticket number: 00710956
CCON-70459
When you read more than 10 GB of data from Databricks, the mappings intermittently fail with the following error:
[ERROR] Read from Delta table failed
(November 2023)
Databricks ticket number: 2310230030003014

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