Redbird AI syncs your dbt models directly to Looker's semantic layer, validates metric consistency, and keeps documentation in sync. Stop manually updating LookML definitions every time your transformation logic changes.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When dbt models are built or updated, Redbird automatically generates or updates corresponding LookML view files in Looker. Column definitions, data types, and descriptions stay synchronized without manual translation. Analytics engineers deploy once and both systems reflect the same structure.
Redbird compares metric logic defined in dbt_metrics.yml with corresponding Looker measures and dimensions. When discrepancies are detected—like different aggregation logic or filters—the system flags conflicts and suggests corrections. Teams maintain a single source of truth for business metrics.
After dbt runs data quality tests, Redbird surfaces test pass/fail status and freshness checks directly in Looker's Explore interface. Business users see which models have passed validation before building reports. Data trust becomes visible at query time.
Redbird analyzes which Looker Explores and joins business users rely on most, then generates corresponding dbt source and staging model scaffolds. Analytics engineers prioritize transformation work based on actual BI consumption. Documentation flows upstream from usage to schema.
When business teams refine field labels and descriptions in LookML for clarity, Redbird propagates those improvements back to dbt model documentation. Column-level context stays consistent across the analytics stack. Business language drives technical documentation.
Before removing or renaming dbt models, Redbird scans active Looker dashboards and Looks for dependencies. Analytics engineers receive impact reports showing which reports will break and who owns them. Model deprecation becomes coordinated and safe.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize dbt and Looker 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 dbt's YAML schemas, test definitions, and lineage DAGs alongside Looker's LookML syntax, Explore relationships, and semantic layer structure.
Redbird parses dbt model SQL to extract column lineage, aggregation logic, and grain, then maps those structures to LookML dimensions, measures, and join paths. The platform recognizes when a dbt metric calculation should match a Looker measure definition and flags inconsistencies automatically. When schema changes propagate from your warehouse through dbt, Redbird updates corresponding view files, dimension groups, and derived tables in Looker without manual LookML editing.
faster than manually syncing dbt schema changes to LookML view files
Redbird can pull from dbt and Looker 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 dbt or Looker.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from dbt into Looker, or from Looker back into dbt. 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 dbt model runs, test failures, or Looker usage events—and update either system automatically.
Fires when a specific dbt model successfully builds in your data warehouse.
Triggers when a data quality test on a model returns errors or warnings.
Detects when columns are added, removed, or have type changes in a model definition.
Modify description fields in model YAML files with context from downstream usage.
Add or update tags and meta properties based on Looker consumption patterns.
Generate source YAML scaffolds for tables referenced in Looker Explores.
Fires when view files, Explore definitions, or measure logic changes in production.
Triggers when a specific Explore or dashboard tile is queried by users.
Detects when dimension or measure labels are edited in LookML files.
Modify view files to reflect new columns, types, or structure from dbt models.
Generate LookML field definitions based on dbt column metadata and metrics.
Inject data freshness, test status, or lineage information into Explore descriptions.
Sync dbt transformations to Looker's semantic layer automatically. Keep metrics consistent, documentation updated, and your analytics stack connected without manual LookML edits.