Automate data pipeline orchestration and warehouse operations between Airflow and Snowflake. Stop manually writing operators, debugging connection configs, and stitching together DAGs with SQL scripts—let Redbird handle pipeline coordination, data loading, and quality validation across your entire stack.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically kick off Snowflake stored procedures or dbt models when upstream Airflow tasks finish. Redbird monitors task states, passes relevant context like execution dates and dataset versions, and handles retry logic when transformations fail.
Sync DAG run history, task durations, failure rates, and resource usage into Snowflake tables. Build executive dashboards on pipeline SLAs, identify bottlenecks across workflows, and track data freshness metrics alongside business KPIs.
Monitor Snowflake warehouse utilization and query queue depth in real-time. When compute resources are constrained, automatically pause non-critical Airflow pipelines or route workloads to alternative warehouses to prevent cascading failures.
Run automated quality checks on newly loaded Snowflake tables—row counts, null rates, schema drift, referential integrity. Surface validation failures back to Airflow as task failures with detailed error context to prevent bad data propagation.
Automatically export large analytical query results from Snowflake to cost-effective storage when jobs finish. Update Airflow XCom or external metadata tables with file locations, row counts, and checksums for downstream consumption.
Detect when new tables appear in Snowflake information schema or existing schemas evolve. Automatically create or update Airflow pipeline definitions to ingest, transform, or aggregate the data—no manual DAG coding required.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Airflow and Snowflake 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 task dependencies and execution context alongside Snowflake's warehouse architecture, enabling intelligent automation across orchestration and compute layers.
Redbird natively parses Airflow DAG structures, task states, XCom payloads, and connection configs while mapping them to Snowflake databases, schemas, stages, and warehouse resources. The platform understands when a task's output dataset should trigger downstream transformations, how to translate Airflow execution dates into Snowflake partition filters, and which warehouse to route workloads based on pipeline priority. No manual operator development or Python glue code needed.
faster than building custom Airflow operators and Snowflake connectors
Redbird can pull from Airflow and Snowflake 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 Snowflake.
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 Snowflake, or from Snowflake 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 pipeline event in Airflow or warehouse operation in Snowflake.
Fires when an entire workflow finishes with success, failure, or specific exit states.
Triggers when a specific task exhausts retry attempts and enters failed state.
Activates when a task or DAG exceeds its defined service level agreement time window.
Programmatically start a specific DAG with custom config and execution date parameters.
Reset task states to allow re-runs of specific pipeline segments without full DAG restarts.
Modify Airflow variables or connection credentials dynamically based on external events.
Fires when new data arrives in a target table or existing rows are modified via MERGE or UPDATE.
Triggers when a long-running analytical query finishes, with access to result metadata and row counts.
Activates when compute credits, queue time, or active queries cross defined operational limits.
Run DDL, DML, or stored procedures in Snowflake with parameterized values from upstream systems.
Load files from external sources into Snowflake internal or external stages for bulk ingestion.
Dynamically adjust compute resources or pause warehouses based on workload patterns or pipeline schedules.
Join data teams using Redbird to sync Airflow and Snowflake without custom operators or brittle Python scripts. Get orchestration and warehouse automation running in minutes, not sprints.