Redbird AI automates the data flow between your lakehouse and your BI layer. Stop manually exporting pipeline metrics, syncing feature definitions, or building custom scripts to keep Looker dashboards in sync with Databricks compute and ML runs.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When models complete training or inference runs in Databricks, Redbird extracts performance metrics, validation stats, and drift indicators and writes them to tables Looker can query. Keeps stakeholders updated on model health without manual metric exports.
Redbird monitors Databricks job statuses, cluster health, and workflow completion times, then syncs runtime metadata to Looker-accessible tables. Data teams get real-time visibility into ETL health without building custom observability dashboards.
When analysts explore new dimensions or report queries shift in Looker, Redbird detects usage pattern changes and triggers Databricks jobs to refresh feature engineering or model retraining pipelines. Keeps ML systems responsive to business needs.
Redbird pulls feature definitions, lineage, and freshness timestamps from Databricks Feature Store and writes them to LookML-accessible metadata tables. Analysts can see feature provenance and update times directly in Looker exploration views.
When Looker queries hit tables marked for deprecation or schema changes in Databricks, Redbird captures the query patterns and alerts engineering teams. Prevents downstream breakage before stakeholder dashboards fail.
Redbird extracts cluster utilization, DBU consumption, and job runtime costs from Databricks, then writes enriched cost data to tables powering Looker FinOps dashboards. Finance and engineering teams track lakehouse spend without CSV exports.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Databricks and Looker 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 Databricks lakehouse schemas and Looker's LookML semantic layer, so you can automate data flows without mapping fields or writing transformation code.
Redbird parses Databricks Delta Lake schemas, Unity Catalog metadata, MLflow experiment structures, and job orchestration logs. It also interprets Looker's LookML views, explores, dimension definitions, and SQL runner patterns. This dual understanding means Redbird can automatically map ML metrics to dashboard fields, sync feature definitions to BI metadata tables, and route pipeline health data to the right Looker explores without manual schema reconciliation.
faster than building custom Databricks-to-Looker sync scripts
Redbird can pull from Databricks and Looker 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 Looker.
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 Looker, or from Looker 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 job completions, model runs, dashboard queries, or any event across your lakehouse and BI stack.
When a Databricks workflow, notebook, or scheduled job finishes or throws an error.
When an MLflow experiment run logs final metrics or registers a new model version.
When a table in Unity Catalog or Delta Lake adds, removes, or modifies columns.
Start a Databricks workflow, notebook run, or scheduled job with custom parameters.
Insert, update, or upsert rows in a Unity Catalog or Delta Lake table.
Record custom metrics, parameters, or artifacts to an existing MLflow experiment run.
When a Looker dashboard executes a query against the underlying data warehouse.
When analysts pivot, filter, or drill into new dimensions in a Looker Explore.
When a scheduled Looker report or alert completes and sends to stakeholders.
Modify dimension definitions, descriptions, or metadata in a LookML model file.
Trigger a rebuild of a Looker PDT or aggregate table to sync with new source data.
Create and deliver a Looker alert or notification based on external trigger conditions.
Sync Databricks and Looker in minutes. Redbird AI handles schema mapping, metric extraction, and pipeline orchestration so your lakehouse and BI layer stay in sync without custom code.