"dbt Labs is bringing four different AI agents to its platform, each designed for a specific part of the data development process. The Developer agent explains logic, flags duplication, and helps refactor code in VS Code or dbt Studio. The Discovery agent helps find the right datasets and definitions, while the Observability agent monitors jobs and identifies possible causes of problems. Finally, the Analyst agent answers questions about models, jobs, and metrics within dbt Insights."
"The dbt Fusion engine, now in preview for BigQuery, Databricks, Snowflake, and Amazon Redshift, promises significant cost savings for organizations. State-aware orchestration reduces compute costs by approximately 10 percent by only executing models that have actually changed. Organizations can achieve further optimizations by specifying specific data freshness requirements. The engine then determines the most efficient execution path. In early tests, some organizations have achieved more than 50 percent cost savings."
Four specialized AI agents assist data development: Developer explains logic, flags duplication, and refactors code; Discovery finds datasets and definitions; Observability monitors jobs and pinpoints causes; Analyst answers questions about models, jobs, and metrics in dbt Insights. The dbt Fusion engine, previewed on BigQuery, Databricks, Snowflake, and Redshift, uses state-aware orchestration to avoid rebuilding unchanged models, cutting compute costs by about 10 percent and enabling further savings through data freshness constraints; early tests report over 50 percent savings. Fusion adds Apache Iceberg support for cross-platform portability. MetricFlow is released under Apache 2.0 and dbt Insights is generally available. Company leadership emphasized open standards and AI as drivers of the next era of analytics.
Read at Techzine Global
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