dbt Labs

Recently shipped native Amazon Redshift data sharing, plus a dbt-core 1.13 beta that bundles dbt State.

Redshift models can now read and write shared data. dbt materializes across Redshift clusters, serverless workgroups, and AWS accounts without copying data. dbt-redshift also moved off legacy Postgres metadata APIs onto Redshift-native SVV_* views and SHOW APIs, giving faster metadata resolution and better behavior under high concurrency.

The Summit launch set the v2 baseline. dbt v2 and dbt State went GA alongside Fivetran Context Layer, dbt Charts, new Wizard experiences, and an open lakehouse vision. dbt State runs on change rather than schedule, works in your own orchestrator (Airflow, Dagster, GitHub Actions, a laptop) and on Snowflake's dbt projects, across Snowflake, BigQuery, Databricks, and Redshift. v2 added AgentSkills-format SKILL.md installs gated on ai_provider, native Databricks metric views, and an adbc_clickhouse driver. Lake Compute reads Unity Catalog with Databricks PAT or OAuth M2M, read-only; existing local Unity attach options must move from config.lakecompute to config.duckdb.

dbt-core 1.13 beta turns on older flags and pre-bundles State. Breaking: flags introduced in 1.9 and 1.10 now default to true. The dbt-state plugin ships as a dependency but stays opt-in — enable it with --manage-state, DBT_ENGINE_MANAGE_STATE=true, or manage_state in dbt_project.yml. --use-v2-parser delegates parsing to the fusion parser, and MAXIMUM_SEED_SIZE_MIB is now configurable.

Maintenance. v2.0.8 lowered the minimum Python for the dbt and dbt-oss wheels to 3.10 and restored dbt build --write-catalog writing catalog.json. v2.0.5 fixed a BigQuery incremental path that could use REPLACE TABLE instead of MERGE.