Redbird AI connects your workflow orchestration platform with Azure cloud storage to automate data pipeline execution, file movement, and storage operations. Stop manually writing Airflow operators for every Blob Storage interaction and let AI handle the pipeline-to-storage coordination across your data infrastructure.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When Airflow DAGs complete transformation jobs, automatically move processed files from hot Blob Storage to archive tiers. Redbird monitors task completion status and coordinates tier transitions based on pipeline metadata and data freshness policies.
Automatically start data pipeline workflows when CSV, Parquet, or JSON files appear in designated Blob Storage containers. Redbird watches container events and kicks off appropriate DAG runs with file metadata as parameters, eliminating manual pipeline triggering.
Stream task execution logs, DAG run metadata, and error traces from Airflow to Blob Storage for long-term retention and analysis. Redbird structures log data by pipeline, timestamp, and status, making it queryable for debugging and compliance audits.
Pull raw data files from designated Azure containers into Airflow workflows for processing, validation, and loading. Redbird handles connection authentication, file listing, and data transfer coordination, passing file paths and metadata to your DAG tasks automatically.
Monitor for pipeline failures that result in incomplete or corrupt files in Blob Storage. Redbird correlates Airflow task status with expected Azure file outputs, identifying data quality issues and notifying teams when cleanup or reprocessing is needed.
Aggregate Airflow DAG performance data—runtime, success rates, resource usage—and write structured reports to Blob Storage. Redbird transforms execution metadata into analysis-ready formats, organizing reports by date and pipeline for historical trend analysis.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Airflow and Azure Blob Storage 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 context alongside Azure Blob Storage's container hierarchies, metadata tags, and access tiers—no custom operator development required.
Redbird's AI natively understands Airflow's task states, XComs, and scheduling parameters while simultaneously parsing Azure Blob metadata, container naming patterns, and blob properties. It automatically maps DAG task outputs to appropriate container paths, handles connection authentication across Azure storage accounts, and coordinates data movement based on pipeline execution context. The platform recognizes when files represent staged data versus final outputs and routes them accordingly through your storage architecture.
faster than building custom Airflow operators for each Azure storage interaction
Redbird can pull from Airflow and Azure Blob Storage 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 Azure Blob Storage.
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 Azure Blob Storage, or from Azure Blob Storage 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 pipeline event or Azure Blob Storage change, then take action across your entire data stack.
When any Airflow workflow finishes execution, whether successful, failed, or skipped.
When a specific task in a DAG fails and enters retry logic with error context.
When a time-based or sensor-triggered workflow begins execution on schedule.
Write cross-communication data between tasks with file paths or processing metadata.
Programmatically start workflow execution with configuration values and file references.
Update task state to success, failure, or skipped based on external validation results.
When files are added to monitored containers, with metadata about size and content type.
When tags, properties, or custom metadata change on existing storage objects.
When blobs are deleted, moved between paths, or containers are created or removed.
Write pipeline output, logs, or reports to specified blob paths with metadata tags.
Move objects between hot, cool, and archive tiers based on age or usage patterns.
Replicate files across storage accounts or organize data by pipeline stage and environment.
Connect Airflow and Azure Blob Storage in minutes. Let Redbird AI handle the pipeline orchestration and storage coordination so your data team can focus on building workflows, not maintaining integrations.