Sync development activity into your data warehouse and push analytics back to engineering workflows. Stop writing custom ETL scripts, manually exporting commit data, or building one-off pipelines to track engineering metrics.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically capture commits, pull requests, code reviews, and contributor activity into BigQuery tables. Build custom dashboards on deployment frequency, cycle time, and developer productivity without maintaining extraction scripts.
Run SQL analytics on velocity, burndown, and capacity across teams, then automatically update GitHub issues with priority labels and sprint assignments. Keep project management in sync with actual data-driven insights.
Monitor application performance, error rates, and usage patterns in BigQuery. Automatically create GitHub issues with context, severity, and assigned owners when thresholds are breached or anomalies are detected.
Capture build times, test results, deployment success rates, and resource consumption from GitHub Actions into structured BigQuery tables. Analyze pipeline efficiency trends and identify bottlenecks across your entire CI/CD history.
Aggregate test results, code coverage metrics, and performance benchmarks stored in BigQuery. Post detailed summary reports as GitHub PR comments or repository README updates on a scheduled basis.
Track lead time, deployment frequency, change failure rate, and mean time to recovery by syncing PR merges, deployments, and incident tags into BigQuery. Build executive dashboards on engineering effectiveness without custom integrations.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize BigQuery 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 AI understands both BigQuery's analytical schemas and GitHub's development objects, so you can connect warehouse insights with engineering workflows without writing glue code.
Redbird natively understands BigQuery table schemas, partitioning strategies, and nested/repeated fields alongside GitHub's repository structure, pull request metadata, issue tracking, and Actions workflow outputs. Map commits to user dimensions, sync calculated metrics to issue labels, or trigger queries based on deployment events—all in plain English. The AI handles schema evolution, API pagination, and data type conversions between GitHub's REST/GraphQL APIs and BigQuery's columnar storage automatically.
faster than building custom Python scripts for GitHub-to-BigQuery ETL
Redbird can pull from BigQuery 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 BigQuery 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 BigQuery into GitHub, or from GitHub 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 automations from any event in BigQuery or GitHub—from scheduled queries completing to pull requests being merged.
Fire workflows when a BigQuery scheduled query finishes running and new results are available.
Trigger actions when a BigQuery table grows beyond a specified size or row count limit.
Detect when fresh data lands in a specific BigQuery table or partition via streaming or batch loads.
Execute a BigQuery SQL statement with dynamic parameters passed from GitHub events or other triggers.
Write structured data from GitHub webhooks or API responses directly into BigQuery tables.
Save BigQuery query output as CSV, JSON, or Parquet files in GCS buckets for downstream processing.
Start workflows whenever a new PR is created or existing pull requests receive new commits or comments.
Trigger actions when commits are pushed to any branch or specific branches like main or production.
Fire automations when CI/CD pipelines finish, whether successful, failed, or cancelled.
Open new GitHub issues with custom titles, bodies, assignees, and labels based on BigQuery analytics.
Add automated comments to PRs with data-driven insights, test results, or performance metrics from BigQuery.
Commit changes to files like README badges, status dashboards, or configuration based on warehouse data.
Sync BigQuery and GitHub in minutes. Stop maintaining custom scripts and start automating engineering analytics, deployment tracking, and data-driven development workflows.