Redbird AI connects your workflow orchestration platform to your data warehouse, automating pipeline monitoring, query execution, and warehouse operations. Stop writing custom operators and manually syncing pipeline metadata with warehouse state.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Monitor Redshift system tables for data load status changes and automatically kick off downstream Airflow workflows when new data arrives. Surface load failures as DAG alerts with warehouse error context for faster debugging.
Stream DAG run metadata, task durations, retry counts, and failure patterns into Redshift for long-term pipeline observability. Build dashboards on pipeline SLAs, resource utilization, and data freshness across your orchestration layer.
Automatically adjust DAG schedules and task priorities based on Redshift query patterns and table access frequency. Keep warehouse data freshness aligned with actual consumption without manual coordination between teams.
Automatically capture task execution details, parameter values, and data lineage information from Airflow runs into structured Redshift tables. Maintain queryable audit trails of all data transformations for governance and regulatory requirements.
Cross-reference Airflow DAG delays with Redshift query queuing and execution times to identify when upstream pipeline issues are degrading warehouse performance. Route context-rich alerts to the right teams based on pipeline ownership metadata.
Detect when Redshift tables need vacuuming, analyzing, or distribution key optimization and automatically create or trigger Airflow DAGs to perform warehouse maintenance during off-peak hours. Keep warehouse performance optimized without manual intervention.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Airflow and Redshift 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 Airflow's DAG structure, task dependencies, and execution metadata alongside Redshift's system tables, query patterns, and warehouse operations.
Redbird automatically maps Airflow task instances, XComs, and DAG runs to Redshift system views like STL_LOAD_ERRORS, SVV_TABLE_INFO, and STL_QUERY. Our AI understands pipeline lineage, warehouse table dependencies, and how orchestration state relates to data availability. Connect execution context from Airflow with query performance metrics from Redshift without building custom operators or parsing log formats.
faster than building custom Airflow-Redshift operators
Redbird can pull from Airflow and Redshift 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 Redshift.
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 Redshift, or from Redshift 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 workflows from any Airflow execution event or Redshift warehouse operation, then take action in either system automatically.
Trigger when an Airflow DAG finishes with success, failure, or specific exit states.
Detect when specific Airflow tasks fail, exceed retry limits, or enter error states.
Trigger when DAG execution duration exceeds defined SLA thresholds or time windows.
Programmatically start Airflow DAG runs with dynamic configuration and context.
Write custom metadata, tags, or lineage information to Airflow task instances.
Automatically enable or disable DAG schedules based on external conditions.
Detect when COPY, INSERT, or UNLOAD operations finish in Redshift with success or error states.
Trigger when specific queries or query patterns take longer than expected to complete.
Detect when Redshift cluster reaches disk space, concurrency, or resource utilization limits.
Run parameterized queries, DDL operations, or maintenance commands in Redshift.
Modify table descriptions, column comments, or custom metadata in Redshift catalogs.
Schedule warehouse maintenance tasks based on table usage patterns or performance metrics.
Stop building custom Airflow operators to manage Redshift workflows. Redbird connects your orchestration layer to your data warehouse with AI that understands both systems.