Redbird AI syncs your Airflow pipelines with MongoDB collections automatically. Stop manually writing operators to extract documents, building custom sensors for collection changes, or maintaining brittle connection configs across DAGs.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Watch MongoDB collections for new documents or schema changes and automatically trigger corresponding Airflow DAGs. Redbird monitors collection metadata and document patterns, starting your transformation pipelines the moment operational data lands without manual sensors or polling logic.
After your Airflow DAGs complete transformations, aggregations, or enrichment steps, automatically write the results back to MongoDB collections. Redbird handles connection pooling, batch sizing, and error handling so your operational applications always have fresh analytical data.
Orchestrate incremental extracts from MongoDB collections through Airflow DAGs that sync to Snowflake, BigQuery, or Redshift. Redbird tracks watermarks, handles nested document flattening, and manages the full pipeline schedule without custom Python operators.
Monitor Airflow task failures and automatically check downstream MongoDB collections for data freshness issues. Redbird correlates DAG run status with expected collection updates, sending contextual alerts when pipelines fail and operational databases go stale.
Trigger Airflow DAGs that read MongoDB documents, call third-party APIs for enrichment data, and write enhanced documents back to collections. Redbird manages the full cycle—extracting documents, orchestrating API calls with rate limiting, and upserting results—without custom operators.
Pull DAG run history, task durations, and failure rates from Airflow alongside MongoDB collection growth, query performance, and document counts. Redbird correlates pipeline orchestration metrics with operational database health to surface bottlenecks and data quality issues.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Airflow 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 Airflow's DAG structure and task dependencies alongside MongoDB's document schemas, indexes, and collection hierarchies.
Redbird's AI automatically parses your Airflow DAG definitions to understand task dependencies, schedules, and operators. It simultaneously inspects MongoDB collection schemas, detecting nested structures, array fields, and document patterns. This means you can trigger pipelines based on document changes, flatten nested MongoDB data for warehouse loads, or write pipeline outputs back to the right collections—all without writing custom hooks or operators. Redbird handles connection management, credential rotation, and schema evolution automatically.
faster than building custom Airflow MongoDB operators
Redbird can pull from Airflow 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 Airflow 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 Airflow into MongoDB, or from MongoDB back into Airflow. 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 Airflow DAG event or MongoDB collection change—Redbird handles the orchestration logic between systems.
Trigger workflows when any Airflow DAG finishes all tasks without failures.
Detect when specific Airflow tasks fail after all retry attempts are consumed.
Monitor DAG runs that exceed configured service-level agreement timing thresholds.
Start a specific Airflow DAG execution with custom configuration and runtime parameters.
Programmatically enable or disable DAG scheduling based on external conditions.
Reset failed or skipped task states to retry pipeline segments without full DAG reruns.
Detect when documents are added to specified MongoDB collections in real-time.
Monitor collections for new fields, index additions, or document structure modifications.
Trigger workflows when a collection reaches a specified number of documents.
Write new documents or upsert existing ones to MongoDB collections with merge logic.
Execute MongoDB aggregation queries and write computed results to target collections.
Query documents by date range and move historical records to S3, GCS, or data warehouses.
Connect Airflow and MongoDB in minutes. Redbird handles the orchestration logic, connection management, and schema mapping so your data pipelines run reliably without custom operator code.