Redbird AI automates the connection between your Azure data lake and dbt transformation layer. Stop manually monitoring blob uploads, writing custom ingestion scripts, or building fragile pipelines to move data from storage to your warehouse for transformation.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically detect new CSV, JSON, or Parquet files uploaded to specific Azure Blob containers and trigger targeted dbt model runs. Redbird monitors your storage paths, validates file schemas match your dbt sources, and initiates incremental model refreshes without manual orchestration.
Capture failed test results, compiled SQL queries, and row-level samples from dbt test runs and store them in Azure Blob Storage. Analytics engineers can review historical test failures and track data quality issues over time without cluttering warehouse tables.
Monitor Azure Blob containers serving as your data lake landing zone and update dbt source freshness metadata in real-time. When upstream data files are delayed or missing, Redbird alerts your team before downstream models break.
When new file types appear in your blob storage, Redbird analyzes the schema and auto-generates skeleton dbt staging models with appropriate column types and source configurations. Reduces the manual work of scaffolding models for each new data source.
Automatically publish compiled dbt documentation artifacts, lineage graphs, and data dictionaries to Azure Blob Storage after each production run. Makes dbt-generated documentation accessible to teams who don't have direct warehouse access.
Redbird compares incoming blob file schemas against your dbt source definitions and alerts analytics engineers when column types change, fields are removed, or unexpected data formats arrive. Catch breaking changes before they cause model failures during transformation.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Azure Blob Storage 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 unstructured blob storage patterns and dbt's declarative transformation framework, intelligently bridging raw file ingestion with analytics-ready modeling.
Redbird automatically parses file formats in your Azure Blob containers—CSV headers, JSON structures, Parquet schemas—and maps them to your dbt source YML definitions. It detects when blob file layouts match or deviate from expected dbt sources, validates column-level compatibility, and identifies which dbt models depend on specific storage paths. No need to manually sync metadata or write custom schema validation scripts.
faster blob-to-warehouse workflows than manual file monitoring and custom ingestion scripts
Redbird can pull from Azure Blob Storage 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 Blob Storage 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 Blob Storage into dbt, or from dbt back into Azure Blob Storage. 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 any blob storage event or dbt workflow step—Redbird connects the full data pipeline from landing zone to transformation layer.
Fires when files matching specific patterns or paths are added to Azure Blob Storage containers.
Triggers when uploaded file structure doesn't match predefined column types or field names.
Activates when blob container size or file count crosses defined limits for archival or cleanup.
Organize blobs across containers based on workflow logic, like archiving processed files.
Apply custom metadata tags to files for downstream processing categorization or compliance tracking.
Remove files from containers after successful ingestion or based on retention policies.
Fires when specific dbt models or full project runs finish, whether successful or failed.
Triggers when data quality tests fail, capturing test names and affected row counts.
Activates when dbt detects source data is stale or missing based on freshness thresholds.
Execute specific dbt models or model selectors on-demand based on upstream data availability.
Programmatically refresh source freshness checks to reflect real-time data arrival patterns.
Auto-create new staging or intermediate models with templated SQL based on source definitions.
Redbird AI eliminates the custom code between Azure Blob Storage and dbt. Start automating your data lake to transformation workflows in minutes, not weeks.