Redbird AI automates the sync between BigQuery and dbt — no more manual schema updates, copy-paste SQL snippets, or forgotten model rebuilds when warehouse structures change. Keep your transformation layer in sync with your data warehouse automatically.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When new tables land in BigQuery or schemas change, Redbird generates matching dbt source definitions and staging models. Your analytics engineers skip the manual YAML updates and boilerplate SQL, and your transformation layer stays current with upstream changes.
After dbt runs finish, Redbird kicks off dependent BigQuery processes like materialized view refreshes or downstream ML model training. You eliminate scheduling gaps and ensure your entire pipeline stays synchronized without custom orchestration code.
Redbird connects dbt test results with BigQuery data quality checks, surfacing when transformation issues stem from upstream data problems. Data teams get context-rich alerts instead of hunting through logs across both systems.
Redbird analyzes your BigQuery audit logs to identify frequently-used business calculations, then drafts dbt metrics YAML with proper aggregations and dimensions. You standardize metrics across tools without manually reverse-engineering SQL from analyst queries.
After each dbt run, Redbird enriches model documentation with actual BigQuery slot usage, bytes processed, and query costs. Analytics engineers see warehouse performance impacts directly in their dbt docs without switching to GCP console.
Before significant BigQuery schema migrations, Redbird captures complete dbt lineage graphs and model definitions. You maintain an audit trail of how transformation logic evolved alongside warehouse structure changes for compliance and debugging.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize BigQuery and dbt with OAuth or API credentials. Redbird never stores your data — it just passes through.
Tell Redbird what to do in plain language — no SQL, no code, no configuration files required.
Redbird shows you exactly what it will do before running anything. Approve the workflow, set a schedule, and switch it on.
Workflows run on your schedule or on triggers. Every run is logged. Adjust with natural language at any time.
Redbird understands both BigQuery's table schemas and partitioning strategies alongside dbt's model dependencies, test definitions, and compilation outputs — so automation works with your actual data structures.
Redbird parses BigQuery INFORMATION_SCHEMA metadata, partition configurations, and clustering keys alongside your dbt project's model files, sources.yml, and schema.yml definitions. It understands how dbt refs map to BigQuery tables, which models depend on partitioned sources, and where schema changes will break compilation. When warehouse structures shift, Redbird knows exactly which dbt models need updates and generates the correct SQL dialect for BigQuery's specific syntax requirements.
faster schema propagation from warehouse to transformation layer
Redbird can pull from BigQuery and dbt simultaneously, merge the results, and format a polished report — sent on a schedule or on demand.
Set conditions in natural language. Get notified in Slack or email the moment a threshold is crossed in either BigQuery or dbt.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from BigQuery into dbt, or from dbt back into BigQuery. Resolve conflicts with configurable merge rules.
Every workflow run is logged — what ran, what changed, and why. Replay or revert any individual step at any time.
Start workflows from any BigQuery table change or dbt run event, then take action across both systems automatically.
Fires when a new table appears in a monitored BigQuery dataset or project.
Triggers when columns are added, removed, or changed in existing BigQuery tables.
Activates when a BigQuery query or job crosses defined cost or slot usage limits.
Execute custom SQL against BigQuery datasets with dynamic parameters from workflow context.
Modify BigQuery table descriptions, labels, or schema field documentation programmatically.
Generate new BigQuery materialized views based on query definitions from other systems.
Fires when a dbt job finishes successfully, with failure, or with warnings.
Triggers when any dbt test returns failures for specific models or test types.
Activates when dbt detects source data hasn't updated within expected timeframes.
Create new dbt staging models with proper column selection and type casting for sources.
Modify dbt sources.yml files to reflect new tables, columns, or freshness requirements.
Insert or update descriptions in schema.yml for models, columns, and tests automatically.
Stop manually syncing BigQuery schemas with dbt models. Redbird AI keeps your warehouse and transformation layer aligned so your team ships analytics faster.