Redbird AI automates data movement and pipeline orchestration between your GCP data warehouse and lakehouse platform. Stop writing custom ETL scripts to sync tables, trigger ML jobs, or reconcile datasets across environments.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically replicate analytical tables from BigQuery to Databricks Delta Lake when queries finish or on cadence. Redbird maps schema changes, handles partitioning strategies, and maintains data freshness without manual export jobs.
Launch model retraining pipelines in Databricks whenever feature engineering queries complete in BigQuery. Redbird monitors table metadata, detects updates, and kicks off notebook runs with the latest feature sets.
Push batch inference results and model scores from Databricks into BigQuery tables for Looker dashboards and stakeholder reporting. Redbird handles schema alignment, upserts, and keeps prediction tables current.
Move aged analytical datasets from BigQuery to cost-efficient Delta Lake storage in Databricks. Redbird orchestrates exports, applies compression, and maintains queryable archives without storage bloat in your warehouse.
Monitor row counts, aggregations, and checksums between BigQuery source tables and Databricks transformed outputs. Redbird flags discrepancies in Slack or email so data teams catch pipeline drift immediately.
Pull ML-derived features from Databricks feature stores and append them to BigQuery customer dimension tables. Redbird joins on customer IDs, handles type casting, and keeps analytical tables enriched with real-time model signals.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize BigQuery 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 AI understands BigQuery's table metadata and partitioning alongside Databricks Delta Lake schemas, so your warehouse and lakehouse environments stay in sync without brittle scripts.
Redbird maps BigQuery table definitions, nested records, and partitioned datasets to Databricks Delta tables, catalogs, and metastore objects. It detects schema evolution on both sides, suggests merge strategies for type mismatches, and auto-generates transformation logic when moving between SQL-first analytics and Spark-based processing. No more manual column mapping or CSV intermediaries.
faster than writing custom BigQuery export and Databricks ingest scripts
Redbird can pull from BigQuery 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 BigQuery 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 BigQuery into Databricks, or from Databricks back into BigQuery. 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 query completions in BigQuery or job runs in Databricks—Redbird handles the rest.
Fire workflows when BigQuery scheduled queries finish running and update destination tables.
Detect inserts, updates, or deletes in BigQuery tables based on row count thresholds.
Trigger automations when teams provision new BigQuery datasets or tables in your project.
Execute BigQuery queries with dynamic parameters and capture result sets for downstream use.
Write BigQuery tables to GCS buckets in Parquet, Avro, or CSV for cross-platform sharing.
Modify BigQuery table definitions programmatically to accommodate new fields or data types.
Start workflows when notebook runs, ML pipelines, or scheduled jobs complete in Databricks.
Monitor Delta Lake tables for new commits, partitions, or streaming appends in your lakehouse.
Trigger actions when models move to production stage or new versions land in the registry.
Kick off Databricks notebooks programmatically, passing runtime variables and job configurations.
Insert or merge records into Databricks Delta tables with automatic schema enforcement.
Start batch scoring jobs in Databricks using registered MLflow models on new datasets.
Sync BigQuery and Databricks in minutes. Redbird AI handles schema mapping, incremental loads, and bi-directional workflows so your data and ML teams work from a single source of truth.