BigQuery Adds AI-Powered Causal Effect Analysis and Data Engineering Enhancements
BigQuery now introduces new capabilities for advanced data analysis and engineering pipelines. Users can leverage the AI.CAUSAL_EFFECT function to quantify intervention impacts on time series data, which is currently in Preview. The Data Engineering Agent gains General Availability integration with BigQuery Graph, enhancing schema mapping accuracy for pipelines. Additionally, conversational analytics in BigQuery now supports the ML.CORRELATION function for statistical analysis between columns, also available in Preview.
Features (3) ›
- BigQuery
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
The Data Engineering Agent now integrates with BigQuery Graph to provide additional context between your data source and destination schema, and improves schema mapping accuracy for your data engineering pipelines. This feature is generally available (GA).
- BigQuery
Conversational analytics in BigQuery now supports the ML.CORRELATION function to calculate statistical correlations between a target column and one or more metric columns in a table. This feature is in Preview .
https://docs.cloud.google.com/release-notes#September_10_2026
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