Redbird AI automates the flow between your orchestration layer and storage layer. Stop writing custom S3 operators, manually staging files between pipeline steps, or building one-off scripts to move processed data into your lake.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When Airflow DAG tasks complete successfully, Redbird automatically uploads output files to S3 with structured prefixes based on run date, pipeline name, and execution status. Maintains organized data lake storage without custom upload logic in every task.
Redbird monitors specified S3 prefixes for new object creation events and automatically triggers corresponding Airflow DAGs with file metadata as parameters. Enables event-driven pipeline architectures without polling or custom sensors.
Each Airflow task writes its output to S3, and Redbird ensures downstream tasks automatically receive the correct file paths and locations. Eliminates XCom size limitations and provides durable inter-task storage for large datasets.
When Airflow tasks fail or data quality checks don't pass, Redbird automatically relocates problematic files to designated S3 quarantine locations with detailed failure metadata. Teams can review and reprocess without affecting production data flows.
Redbird analyzes S3 object creation patterns, sizes, and frequencies from Airflow-managed pipelines and triggers alerts when anomalies appear. Detects missing outputs, unexpected file sizes, or delayed processing before downstream consumers are impacted.
When teams update pipeline configuration JSON or YAML files in S3, Redbird automatically propagates those changes to corresponding Airflow DAG variables and connections. Enables GitOps-style configuration management without manual variable updates.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Airflow and Amazon S3 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 lifecycle, and S3's object hierarchy, metadata, and event patterns — connecting orchestration logic with storage reality.
Redbird's AI interprets Airflow task contexts, XCom values, and execution dates, then maps them to S3 bucket layouts, prefixes, and object metadata. It understands partitioning schemes, compression formats, and file naming conventions across both systems. The AI automatically constructs correct S3 paths from DAG parameters and builds appropriate trigger conditions from S3 event patterns without requiring custom code.
faster pipeline-to-storage automation than custom S3 operators
Redbird can pull from Airflow and Amazon S3 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 Amazon S3.
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 Amazon S3, or from Amazon S3 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 task event or S3 object change, and take action across both platforms.
Fires when an entire Airflow DAG finishes execution with success, failure, or specific status.
Triggers when a specific task within a DAG completes successfully and outputs are ready.
Activates when an Airflow task fails after all configured retry attempts are depleted.
Modify Airflow variables that control pipeline behavior, thresholds, or configuration.
Initiate a specific DAG run and pass custom configuration parameters from external events.
Reset specific task instances to allow reprocessing without full DAG re-runs.
Fires when a new file is uploaded to a specified S3 bucket or prefix path.
Activates when files are removed from specified S3 locations, indicating cleanup or data lifecycle events.
Triggers when total storage in a bucket or prefix crosses a defined size limit.
Write data, reports, or pipeline outputs to specified S3 locations with custom metadata.
Move or replicate files across S3 buckets for staging, archival, or cross-region distribution.
Modify S3 object tags, content-type, or custom metadata without re-uploading files.
Automate the connection between Airflow orchestration and S3 storage. Build pipeline-to-lake workflows that understand both systems without writing integration code.