Automate data flows between your lakehouse and data warehouse. Stop manually exporting Delta tables, writing glue scripts, and rebuilding feature sets. Redbird AI syncs Databricks to Redshift—and back—with intelligent orchestration.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically push curated Delta tables from Databricks to Redshift on a schedule or when transformations complete. Keep your warehouse fresh for Tableau, Looker, and analytics teams without building custom export pipelines.
Pull raw event and transaction tables from Redshift into Delta Lake for feature extraction and model training. Redbird handles incremental loads and schema evolution so data scientists work with complete, up-to-date datasets.
Move aging transactional data from expensive Redshift storage to Databricks lakehouse for long-term retention. Keep full query access through federated queries while reducing warehouse costs on cold data.
Write model predictions and scoring outputs from Databricks directly into Redshift tables. Power real-time dashboards and business intelligence with ML-enriched data without manual CSV exports or S3 staging.
Monitor critical data pipelines and notify teams immediately when scheduled syncs miss SLAs or schema conflicts block warehouse updates. Keep downstream analytics reliable without constant pipeline babysitting.
Keep ML feature stores current by pulling pre-aggregated metrics and dimensions from Redshift into Databricks Feature Store. Redbird orchestrates incremental updates and validates feature consistency across environments.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Databricks 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 AI understands both Delta Lake schemas and Redshift table structures, translating between lakehouse and warehouse formats without brittle glue code.
Redbird maps Databricks Delta tables, partitions, and data types to Redshift distribution keys, sort keys, and column encodings automatically. It handles Spark DataFrame schemas, nested structs, and array types, converting them to Redshift-optimized formats. Schema drift is detected and reconciled across both systems, and incremental sync strategies adapt to your partition schemes and query patterns.
faster than building lakehouse-warehouse sync pipelines with Glue, Airflow, and custom scripts
Redbird can pull from Databricks 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 Databricks 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 Databricks into Redshift, or from Redshift back into Databricks. 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 Databricks job completion or Redshift table change, then automate actions across your entire data platform.
Trigger when a Delta Lake table receives new records or a partition is written.
Fire when a scheduled notebook, pipeline, or model training run finishes successfully.
Activate when a new model version is logged to MLflow Model Registry.
Insert or merge data into a Delta table with automatic schema evolution.
Start a notebook or job execution with custom parameters and dependencies.
Refresh feature tables with new values from upstream data sources.
Trigger when new rows are inserted into a warehouse table via COPY or INSERT.
Fire when a scheduled analytic query or materialized view refresh finishes.
Activate when a fact or event table reaches a defined volume for archival or aggregation.
Execute optimized COPY commands with compression and distribution key handling.
Execute aggregations, transformations, or maintenance commands in Redshift.
Provision new tables or alter schemas based on upstream data structure changes.
Join data teams who've eliminated manual lakehouse-to-warehouse pipelines. Sync Databricks and Redshift with AI-powered automation that adapts to your schemas and scales with your data.