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Copy file name to clipboardExpand all lines: _posts/2026-04-23-debezium-3-6-alpha1-released.adoc
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@@ -41,7 +41,7 @@ Downstream consumers and schema registry entries may need to be updated accordin
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The PostgreSQL connector now returns enum values in their logical PostgreSQL sort order rather than their internal storage order, ensuring that the allowed values list in the emitted schema metadata is stable and predictable across connectors and restarts.
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Previously, two connectors capturing the same enum type could produce different value orderings, causing Avro schema mismatches for downstream consumers sharing a topic.
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Because the order of enum values in emitted schemas may change after upgrading, users relying on a specific ordering in their schema registry or downstream consumers should review and, if necessary, reconcile their schemas before upgrading ((https://github.com/debezium/dbz/issues/1331[dbz#1331])).
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Because the order of enum values in emitted schemas may change after upgrading, users relying on a specific ordering in their schema registry or downstream consumers should review and, if necessary, reconcile their schemas before upgrading (https://github.com/debezium/dbz/issues/1331[dbz#1331]).
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Note that when new values are inserted between existing enum values in PostgreSQL, the resulting allowed values list will reflect that logical position rather than appending to the end.
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@@ -89,7 +89,7 @@ The metric is exposed consistently across all connectors that support the `skip.
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Debezium includes a new single message transformation (SMT) that integrates Docling, allowing selected text fields in change events to be converted into a unified document representation format.
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By leveraging Docling's document processing capabilities within the Debezium pipeline, users can normalize and enrich textual content as it streams from source databases to downstream systems.
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This is particularly useful for AI and data integration workflows where consistent, structured document output is required for downstream processing such as indexing, embedding generation, or retrieval-augmented generation ((https://github.com/debezium/dbz/issues/1442[dbz#1442]).
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This is particularly useful for AI and data integration workflows where consistent, structured document output is required for downstream processing such as indexing, embedding generation, or retrieval-augmented generation (https://github.com/debezium/dbz/issues/1442[dbz#1442]).
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