Automate data ingestion from Blob Storage into Databricks lakehouse tables. Stop manually mounting containers, writing ingestion scripts, or monitoring file arrivals. Redbird orchestrates your Azure-to-Databricks pipelines with intelligent automation across your entire data stack.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically detect new files landing in Blob Storage containers and trigger Databricks jobs to ingest them into Delta tables. Redbird handles schema inference, format conversion, and partition management. Your lakehouse stays current without manual orchestration.
Write Databricks job outputs and historical model artifacts back to Blob Storage with intelligent lifecycle policies. Redbird automatically organizes results by date, job name, and environment. Keep your lakehouse clean while maintaining compliance archives.
Chain Databricks transformations triggered by file arrivals across multiple Blob Storage containers. Redbird coordinates staging, processing, and output steps with data quality checks between stages. Build complex pipelines without Airflow or custom orchestration.
Automatically load new training datasets from Blob Storage into Databricks feature tables when data teams upload files. Redbird validates schemas, handles incremental updates, and triggers retraining workflows. Keep ML pipelines fed with fresh data.
Schedule SQL warehouse queries and push results to specific Blob Storage paths for consumption by external applications. Redbird handles format conversion, partitioning, and access coordination. Power dashboards and APIs from your lakehouse without manual extracts.
Monitor ingestion jobs, detect stalled file arrivals in Blob Storage, and get notified when Databricks workflows fail. Redbird correlates events across both systems to identify root causes. Troubleshoot data pipeline issues before stakeholders notice.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Azure Blob Storage and Databricks 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 Azure Blob Storage container structures and Databricks lakehouse schemas, so you can orchestrate data flows without writing ingestion code or managing mount configurations.
Redbird automatically maps Blob Storage container paths, file formats, and partition structures to Databricks catalog schemas and Delta Lake tables. It infers schema changes from Parquet and CSV files, manages incremental loads, and handles format conversions. You get intelligent routing from blob paths to the right database, schema, and table without manual configuration or Spark notebooks.
faster pipeline setup than mounting containers and writing PySpark ingestion code
Redbird can pull from Azure Blob Storage and Databricks 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 Azure Blob Storage or Databricks.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from Azure Blob Storage into Databricks, or from Databricks back into Azure Blob Storage. 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 from any blob upload or Databricks job event and automate across your entire Azure data stack.
Fires when a new file is uploaded to a specified container or path prefix.
Detects when an existing blob is updated with new content.
Triggers when total storage in a container exceeds a specified limit.
Write data to a specific blob path with automatic partitioning and metadata tagging.
Move or replicate files across containers with pattern matching and filtering.
Change blob access tier based on age or usage patterns to optimize storage costs.
Fires when a scheduled or triggered Databricks workflow finishes successfully.
Detects when new data is written to a Unity Catalog table.
Triggers when a Databricks notebook or job encounters an error or timeout.
Trigger a workflow with dynamic inputs like file paths, table names, or date ranges.
Run SQL statements against Unity Catalog tables and capture results.
Insert or merge data into catalog tables with automatic schema evolution.
Automate data flows between Azure Blob Storage and Databricks without custom scripts or orchestration overhead. Redbird handles ingestion, transformation triggers, and lakehouse coordination across your Azure data platform.