Redbird AI syncs Azure SQL to dbt automatically — extracting source data, orchestrating transformations, and keeping your analytics models in sync with production databases. Stop manually exporting tables, writing custom extraction scripts, or maintaining brittle pipelines between your operational data and transformation layer.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When new tables or columns are added to Azure SQL, Redbird detects schema changes and automatically generates or updates dbt source YAML files with accurate schema definitions. Analytics engineers always work with current source metadata without manual documentation.
Monitor Azure SQL for data changes in key operational tables and automatically trigger targeted dbt model runs to refresh downstream analytics. Keep dashboards and reports current without over-running transformations or building custom orchestration.
When dbt tests fail during transformation runs, Redbird writes failure details, affected models, and error context to Azure SQL tracking tables. Data teams can query test history directly in SQL and build operational dashboards around data quality metrics.
Redbird analyzes Azure SQL table schemas and data patterns to auto-generate initial dbt staging models with appropriate type casting, null handling, and column renaming. Accelerate onboarding new source systems into your analytics warehouse without manual SQL writing.
After each dbt run, Redbird captures execution metadata, model lineage graphs, and transformation logic, then writes structured records to Azure SQL. Maintain a queryable audit trail of all analytics transformations for compliance and impact analysis.
Pull aggregated customer metrics, lifetime values, or segmentation flags from dbt analytics models and write them back to Azure SQL operational tables. Enable production applications to leverage analytics-layer calculations without complex joins or duplicated logic.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Azure SQL 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 AI understands both Azure SQL relational schemas and dbt project structures — reading table definitions, model dependencies, test configurations, and transformation logic across your data stack.
Redbird parses Azure SQL system views to understand tables, columns, indexes, and relationships, then maps them to dbt source definitions and model references. It reads your dbt_project.yml configuration, model SQL files, schema tests, and manifest.json artifacts to understand transformation lineage. When schemas change in Azure SQL, Redbird identifies impacted dbt models and can auto-update source definitions or suggest model modifications. The AI interprets both T-SQL stored procedures in Azure and Jinja-templated SQL in dbt to maintain consistency across operational and analytical layers.
faster than building custom extraction scripts and dbt source generators
Redbird can pull from Azure SQL 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 Azure SQL 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 Azure SQL into dbt, or from dbt back into Azure SQL. 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 automations from schema changes in Azure SQL or model runs in dbt — then take action across your entire analytics stack.
Fires when columns are added, removed, or modified in tracked Azure SQL tables.
Triggers when a table exceeds a specified row count, indicating new data batches.
Detects when new tables, views, or stored procedures are added to Azure SQL.
Run custom T-SQL queries against Azure SQL and return results for downstream use.
Insert or update records in Azure SQL tables with pipeline metadata or lineage information.
Generate point-in-time snapshots of Azure SQL tables for versioning or audit trails.
Fires when a dbt model finishes execution, whether successful or failed.
Triggers when dbt data quality tests fail during transformation runs.
Detects when dbt source freshness thresholds are exceeded for tracked tables.
Execute targeted dbt model runs with custom selection syntax and configuration.
Modify dbt source YAML files with new schema definitions or metadata properties.
Trigger dbt docs generation and deploy updated documentation to hosting environments.
Sync Azure SQL and dbt automatically with Redbird AI. Stop writing extraction scripts and start building analytics models that stay in sync with production data.