Releasesby GlyphBigQuery ML.METRICS evaluates predictions without a stored model
The preview ML.METRICS function computes classification or regression evaluation metrics on any table or query with actual and predicted values, with no need to create or reference a stored model.
Also shipped Sep 10, 2026Google Cloud in September 2026 →
Using dbt artifacts for Slim CI and defer to production in dbt Projects on Snowflake (General availability)
Slim CI, defer to production, failed-execution recovery, and concurrent execution are now generally available for dbt Projects on Snowflake. Reuse dbt artifacts from recent production executions to accelerate development, streamline CI/CD workflows, and recover from failed production runs without maintaining a separate artifact store.
External secret providers (Preview)
External secret providers are now in preview. If your organization already keeps API keys, database passwords, and similar values in AWS Secrets Manager, Azure Key Vault, or Google Cloud Secret Manager, Snowflake can read those values when a query needs them, so you don't have to maintain a second copy in a Snowflake secret .
Iceberg Scan Plan API in Horizon Catalog (Public Preview)
Public Preview release of Iceberg Scan Plan API in Horizon Catalog to enforce data protection policies.
Inherited grants and container-level MANAGE GRANTS (General availability)
Inherited grants and container-level MANAGE GRANTS are now generally available.
Managed tables in Lakeflow pipelines (Beta)
You can now create managed tables in Lakeflow pipelines and write to them with one or more append flows. This feature is in Beta . You can also use import_checkpoint on an append flow to migrate an existing Structured Streaming workload into a pipeline without reprocessing the source. See create_table , CREATE TABLE ... FLOW (pipelines) , and append_flow .
The Databricks Excel Add-in is now generally available
The Databricks Excel Add-in is now generally available. Connect Microsoft Excel to your Databricks workspace to browse and import governed Unity Catalog tables and views, run SQL queries, build pivot tables, use custom functions, and write data back to Unity Catalog.
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BigQuery conversational analytics adds ML.CORRELATION in preview
Conversational analytics can now use ML.CORRELATION to calculate statistical correlations between a target column and one or more metric columns in a table.
You can use the AI.CAUSAL_EFFECT function to quantify the impact of specific interventions on time series data
You can use the AI.CAUSAL_EFFECT function to quantify the impact of specific interventions on time series data. This feature is in Preview .
BigQuery Data Engineering Agent integrates with BigQuery Graph
The Data Engineering Agent now uses BigQuery Graph for extra context between source and destination schemas, improving schema mapping accuracy in data engineering pipelines. It is generally available.
BigQuery generative AI functions add gemini-3.5-flash-lite, 3.6-flash and 3.7-flash
BigQuery generative AI functions now support the gemini-3.5-flash-lite, gemini-3.6-flash and gemini-3.7-flash models.
Conversational analytics now supports predictive modeling questions using the AI.PREDICT function
Conversational analytics now supports predictive modeling questions using the AI.PREDICT function . This feature is in Preview .
Metadata for BigQuery Graph is automatically ingested and searchable in Knowledge Catalog
Metadata for BigQuery Graph is automatically ingested and searchable in Knowledge Catalog . This feature is available in preview .
BigQuery supports WHERE clauses inside aggregate function calls in preview
Aggregate function calls can now include a WHERE clause that filters their input with a boolean expression, simplifying conditional aggregation in SQL.