Bridge your operational document store and SQL transformation layer automatically. Stop manually exporting MongoDB collections, writing custom ETL scripts, or maintaining brittle sync jobs between your NoSQL data and analytics models.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically detect updates to MongoDB collections and trigger dbt runs to refresh staging models. Redbird flattens nested document structures into columnar formats your dbt models expect, handling schema drift without manual intervention.
Push dbt model outputs—like customer segments, aggregated metrics, or enriched entities—back into MongoDB collections. Enable your application layer to consume analytics-grade data without querying the warehouse directly.
Monitor dbt test results for patterns suggesting upstream problems in MongoDB source data. Automatically log issues back to MongoDB metadata collections or trigger notifications to data producers when operational data doesn't meet analytical standards.
Detect when MongoDB document schemas evolve—new fields, nested arrays, or data type changes. Redbird auto-generates updated dbt source definitions and suggests model modifications, keeping your transformation layer aligned with operational reality.
Take customer lifetime value, engagement scores, or other analytics calculated in dbt and write them back as fields in MongoDB user documents. Keep operational systems enriched with warehouse-computed insights without custom API integrations.
Coordinate dbt snapshot runs with MongoDB change streams to maintain parallel historical records. Redbird ensures your slowly-changing-dimension logic in dbt accurately reflects document evolution in MongoDB over time.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize dbt and MongoDB 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 MongoDB's flexible document schemas and dbt's SQL-based transformation patterns—automatically handling the translation layer between NoSQL and analytical models.
Redbird's AI parses nested MongoDB documents—arrays, embedded objects, varied field types—and generates the flattening logic your dbt models need. It understands dbt's ref() functions, test configurations, and documentation standards, creating source definitions and staging models that handle schema evolution. When MongoDB structures change, Redbird detects drift and suggests model updates, eliminating the manual grind of keeping SQL transformations aligned with evolving document schemas.
faster than writing custom Python ETL scripts to sync MongoDB and dbt
Redbird can pull from dbt and MongoDB 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 MongoDB.
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 MongoDB, or from MongoDB 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 any dbt model run or MongoDB document change, and automate what happens next across your entire stack.
Trigger when any specified dbt model successfully builds or refreshes.
Detect when data quality tests fail on models or sources.
Start workflows when project documentation is built and metadata updates.
Execute dbt runs for selected models, tags, or full project refreshes.
Modify source YAML files to reflect schema changes or new sources.
Generate new staging or intermediate models using standardized SQL patterns.
Trigger when new documents are added to specified collections.
Detect changes to existing documents based on change streams.
Identify when new fields appear or data types evolve in documents.
Insert or upsert documents into collections from transformed data.
Modify specific fields in existing documents based on analytics results.
Execute aggregation pipelines and convert nested output into tabular format.
Stop writing custom scripts to bridge MongoDB and dbt. Let Redbird handle the sync, transformation, and schema management between your operational document store and analytics models.