Redbird AI syncs dbt transformation outputs directly to SQL Server tables and orchestrates model runs based on database changes. Stop manually promoting models to production, copying transformation results between systems, or rebuilding tables when source data updates.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When dbt models complete in your warehouse, Redbird AI automatically syncs the transformed data to corresponding SQL Server tables used by production applications. Keeps operational databases in sync with your analytics layer without manual exports or custom scripts.
Automatically kick off dbt model refreshes when source data changes in SQL Server tables. Redbird monitors database triggers or change data capture events and orchestrates the appropriate dbt models, ensuring your analytics stay fresh without manual intervention or time-based schedules.
Redbird reads your dbt source YAML files and automatically provisions or updates corresponding staging tables in SQL Server. Maintains schema alignment between your transformation layer and operational databases, eliminating schema drift and manual DDL updates.
Apply dbt-defined metrics and business logic to operational SQL Server tables. Redbird executes your standardized metric definitions against production data and writes calculated fields back to the database, ensuring consistent KPIs across analytics and operational systems.
When dbt tests fail, Redbird queries SQL Server metadata tables to gather context about affected records, row counts, and data distributions. Automatically generates detailed failure reports that help analytics engineers quickly diagnose data quality issues at the source.
Redbird monitors SQL Server transactional tables and routes historical records through dbt transformation pipelines to create type-2 slowly changing dimensions. Applies your version-controlled transformation logic while archiving operational data, maintaining data lineage from source to warehouse.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize dbt and SQL Server 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 AI understands both dbt's YAML-based model definitions and SQL Server's schema metadata, automatically mapping transformations to database tables without manual configuration.
Redbird parses your dbt project files to understand model dependencies, source definitions, and column-level transformations. It connects this to SQL Server system tables, constraint definitions, and index structures to intelligently route data between your analytics layer and operational databases. The AI recognizes when a dbt model maps to a SQL Server table by name, lineage, or column patterns, and handles data type conversions, constraint validation, and incremental sync logic automatically.
faster than writing custom Python scripts to sync dbt outputs to SQL Server
Redbird can pull from dbt and SQL Server 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 SQL Server.
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 SQL Server, or from SQL Server 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 results, or SQL Server table updates, stored procedures, and change events.
Triggers when any dbt model successfully completes execution in your data warehouse.
Fires when a dbt test fails validation, capturing test name, model, and failure details.
Triggers when dbt checks source data freshness, including pass/fail status and staleness metrics.
Executes a dbt model or model selector, optionally with full-refresh or specific vars.
Runs dbt docs generate to update project documentation with latest model definitions.
Adds or updates a source YAML definition based on external schema metadata.
Fires when new rows are added to a specified SQL Server table, with optional row count threshold.
Triggers when a specific stored procedure completes, capturing execution time and output parameters.
Fires when DDL changes occur on tracked tables, including column additions, type changes, or constraint updates.
Writes rows to a SQL Server table with upsert logic based on primary key or unique constraints.
Runs a SQL Server stored procedure with specified parameters and captures result sets.
Provisions a new table or modifies existing schema based on source column definitions and data types.
Sync dbt transformations to SQL Server and orchestrate model runs from database events. Redbird AI handles the schema mapping, data validation, and workflow logic automatically.