Knowledge Catalog release notes

Data domains in Knowledge Catalog allow you to logically organize the resources within the enterprise to discover and curate your data at scale. This feature is available in Preview.

For more information, see About data domains.

Knowledge Catalog support for importing metadata from dbt Core and MetricFlow is available in Preview.

You can use the gcloud alpha dataplex dbt metadata-jobs command to extract and import technical, semantic (MetricFlow), operational, data quality, and lineage metadata from dbt Core artifacts into Knowledge Catalog.

For more information, see Import metadata from dbt Core and About metadata connectors.

Governance workflows let you set up automated controls for data product access management by providing a request-review mechanism. This feature is available in preview.

For more information, see About governance workflows.

Data lineage control at the organization, folder, or project level is generally available for BigQuery, Managed Service for Apache Spark, and Managed Service for Apache Airflow.

For more information, see About data lineage ingestion control and Configure data lineage ingestion for a service.

Knowledge Catalog connectors for importing metadata from SQL Server and PostgreSQL data sources are available in Preview.

Knowledge Catalog connectors automatically extract metadata (technical, operational, and business) from external data sources and import it into Knowledge Catalog entry groups. You can schedule metadata import runs on a set schedule.

For more information, see About database connectors and Manage connector jobs.

You can control data lineage ingestion for BigQuery and Managed Service for Apache Airflow at the organization, folder, or project level. This feature is available in preview.

For more information, see Control data ingestion.

Knowledge Catalog connectors for importing metadata from Oracle and MySQL data sources are available in Preview.

Knowledge Catalog connectors automatically extract metadata (technical, operational, and business) from external data sources and import it into Knowledge Catalog entry groups. You can schedule metadata import runs on a set schedule.

For more information, see About database connectors and Manage connector jobs.

Knowledge Catalog automatic discovery of Cloud Storage data is available in GA.

Automatic discovery lets you scan data in Cloud Storage buckets to extract and catalog metadata in Knowledge Catalog. As part of a discovery scan, automatic discovery creates BigLake external tables, non-BigLake external tables, or BigLake object tables in BigQuery, making the metadata available for search and analysis.

For more information, see Discover and catalog Cloud Storage data.

Knowledge Catalog now supports data profile scans for unstructured data (such as PDFs in Cloud Storage) on existing BigQuery object tables. This feature uses Vertex AI Gemini models to extract semantic insights, including entities and relationships, from unstructured content.

For more information, see About unstructured data insights and Use data profile for unstructured data.

Starting June 1, 2026, the Data Catalog service begins a phased shutdown. From this date onward, you might experience disruptions or a complete lack of access to Data Catalog APIs. Knowledge Catalog (formerly known as Dataplex Catalog) operates without impact.

For more information about migrating from Data Catalog to Knowledge Catalog, see Transition from Data Catalog to Knowledge Catalog.

You can use the lookupContext method to retrieve a pre-formatted bundle of data asset context optimized for interactive agentic workflows. This LLM-ready context helps to ground your agents in assessing and using data assets.

This feature is available in preview.

For more information, see Retrieve context for data assets.

The Data Lineage API includes the searchLineageStreaming method that performs a breadth-first search (upstream or downstream) to retrieve lineage links for an asset identified by its Fully Qualified Name (FQN).

For more information, see the Data Lineage API reference for REST.

You can use the data lineage remote MCP server to interact with Knowledge Catalog (formerly Dataplex Universal Catalog) to query data lineage graphs, discover upstream data provenance, and analyze downstream impact.

This feature is available in preview. For more information, see Use the data lineage remote MCP server.

Data products in Knowledge Catalog is Generally Available (GA). A data product serves as a logical, curated package of data assets and context designed to solve a specific business problem.

This release includes the following new features:

• Approval workflows for data product consumption: Data product consumers can browse published data products, submit access requests, and track their status. Data product owners can track, approve, or reject access requests using the Google Cloud Console or the API. For more information, see Use data products and Manage data products.

• Automated documentation and insights: Data product owners can leverage Knowledge Catalog data insights and Gemini to automatically generate sample queries, business insights, and documentation templates for data products. For more information, see Create data products.

• Service account support: Data product owners can configure service accounts in access groups, and data product consumers can request access for their service accounts. For more information, see Create data products.

• Remote Model Context Protocol (MCP) server support (Preview) Data applications and AI agents can programmatically interact with data products. By deploying the Knowledge Catalog remote MCP server, developers can create data products, discover data products, and inspect data product metadata from external IDEs and LLM clients. For more information, see Access data products using Model Context Protocol.

Column-level lineage for Dataproc is generally available (GA). This feature enables you to track the flow of data between individual columns in BigQuery, BigLake external tables, Cloud Storage buckets, and other resources as reported by Dataproc clusters and Serverless for Apache Spark. For more information, see About data lineage.

The Data Lineage API is now updated with the following changes:

• The SearchLinks method accepts multiple source and target entity references as search criteria.
• Added support for column-level lineage information to be passed and returned from the service.
• Process resources now report Dataflow as their origin if it is used to generate lineage.

For more information, see the Data Lineage API reference for REST and RPC.

Cloud Composer is now called Managed Service for Apache Airflow. The names for associated APIs, client libraries, CLI commands, and Identity and Access Management (IAM) remain unchanged and still reference Composer.

Dataproc and Google Cloud Serverless for Apache Spark are now unified under the Managed Service for Apache Spark brand. This change consolidates our managed Spark deployment options into a single umbrella brand that includes the full breadth of our Spark capabilities. No existing functionality is being removed as part of this change, and there is no impact to the Dataproc API, metastore, client library, CLI, or IAM names.

Stop Datascan job in Knowledge Catalog is Generally Available (GA). You can cancel data quality, data profiling, and discovery jobs that are taking longer than expected or were misconfigured. For more information, see Cancel a data quality scan job, Cancel a data profile scan job, and Cancel a Discovery job.

BigLake is now called Google Cloud Lakehouse. BigLake metastore is now called the Lakehouse runtime catalog. The names for associated APIs, client libraries, CLI commands, and Identity and Access Management (IAM) remain unchanged and still reference BigLake.

Knowledge Catalog discovers links between data assets, helping you understand how they connect and the nature of their relationships. This feature is available in preview. For more information, see View data relationships in Knowledge Catalog.

Data quality now supports rule reusability. You can now define data quality rules as templates and reuse them across multiple catalog entries to standardize your data quality processes. You can also use a shared library of system rule templates for common data validation scenarios. For more information, see Reuse data quality rules.

You can now build and run a Knowledge Catalog discovery agent to get more relevant search results for complex natural language queries.

For more information, see Build an agent to discover your data.

To further refine lineage graphs, Knowledge Catalog lineage views include new highlight and filter modes. This feature is available in preview. For more information, see Apply filters and highlighting for a focused view.

Data insights for unstructured data transforms dark data or unstructured files in the form of PDFs in Cloud Storage into structured, queryable assets. This feature is now available in preview.

For more information, see About data insights for unstructured data.

Automated cataloging of Iceberg REST Catalog (IRC) for Google Cloud Lakehouse runtime catalog is now generally available (