Redbird AI syncs your lakehouse workflows with version control automatically. Stop manually tracking which pipelines run from which commits, copying model metadata between systems, or hunting down code changes that broke a production job.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Trigger job updates in Databricks whenever pull requests merge into production branches. Redbird validates notebook compatibility, updates job configurations, and logs deployment metadata back to GitHub issues. Teams ship pipeline changes without manual deployment scripts.
Monitor job run health and automatically open issues in GitHub when pipelines fail repeatedly or error rates spike. Redbird includes cluster logs, data lineage context, and links to affected notebooks. Data engineers triage production incidents faster with complete context.
Publish model registry updates as GitHub releases with complete experiment parameters, metrics, and artifact references. Redbird captures lineage from training notebooks through model deployment. ML teams maintain a unified history of model evolution across both platforms.
Run automated validation checks on pull requests containing Databricks notebooks by executing them in test clusters. Redbird reports execution results, schema changes, and resource usage back as PR comments. Prevent breaking changes from reaching production pipelines.
Capture schema evolution events from Unity Catalog and commit structured documentation to GitHub repositories. Redbird generates markdown changelogs with table lineage, column descriptions, and breaking change warnings. Data consumers stay informed about upstream changes without Slack floods.
Update GitHub issues and project cards with pipeline runtime statistics, cluster costs, and data volume metrics from Databricks. Redbird correlates code deployments with infrastructure spend and performance. Engineering leads track the efficiency impact of pipeline optimizations.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Databricks and GitHub 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 Databricks workspace structures, Unity Catalog lineage, MLflow registries, and GitHub repository hierarchies, branch strategies, and CI/CD workflows natively.
Redbird maps Databricks job definitions to GitHub repository structures, correlates notebook cells with code changes, and parses Delta Lake schema evolution. Our AI understands Unity Catalog namespaces, MLflow experiment hierarchies, and GitHub Actions workflows to route the right context to the right place. Handle complex mappings like syncing Spark SQL schema changes to version-controlled data contracts or linking model training runs to the exact commits that generated them.
faster to deploy pipeline changes than manual CI/CD scripting
Redbird can pull from Databricks and GitHub 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 GitHub.
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 GitHub, or from GitHub 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 from any event in Databricks or GitHub and automate what happens next across your data and development stack.
Trigger when any Databricks job fails, including cluster errors, task failures, or timeout events.
Trigger when a new model version is registered in MLflow or promoted to production stage.
Trigger when Unity Catalog detects schema evolution on monitored Delta Lake tables.
Modify Databricks job parameters, cluster settings, or scheduled triggers programmatically.
Execute specific Databricks notebooks with dynamic parameters and capture execution results.
Apply metadata tags to notebooks, jobs, or clusters for governance and cost tracking.
Trigger when PRs merge to specified branches, with filters for file paths and labels.
Trigger when new releases or tags are created in GitHub repositories.
Trigger when issues receive specific labels or get assigned to team members.
Open GitHub issues with custom templates, labels, assignees, and project board assignments.
Add automated comments to PRs with validation results, test outputs, or approval requests.
Programmatically commit documentation, configuration files, or generated artifacts to repositories.
See how teams sync Databricks pipelines with GitHub workflows in minutes. Redbird handles the complexity of lakehouse-to-repo integration so you can focus on building.