Build production-grade data pipelines

See your entire pipeline as an interactive graph. Inspect inputs and outputs at each node, follow data as it flows between steps, spot bottlenecks at a glance and restart from any step without replaying the full run.

Connect to databases, APIs and third-party services using typed resources. Credentials are stored centrally and injected at runtime. Share connections across scripts and flows without duplicating secrets.

Fan out extraction steps across independent sources with parallel branches and collect results automatically. Configure for-loops with configurable parallelism to process batches concurrently.

Fix a bug and re-run from the failing step in the flow editor. No need to replay the entire pipeline or re-extract data from upstream sources.

Each step can have its own error handling strategy and configurable retries with exponential backoff. Run a custom script on failure (send a Slack alert, create a ticket), stop the pipeline early or mark non-critical steps to continue on error.

Start pipelines from cron schedules, webhooks, Postgres CDC, Kafka, SQS, or manually from the UI. Combine multiple trigger types on the same pipeline.

Built-in relational storage with zero setup via data tables. Query from Python, TypeScript, SQL or DuckDB. Credentials are managed internally and never exposed.

Promote pipelines from draft to production in one click with full deployment history and instant rollbacks. Sync your workspace with GitHub or GitLab, use your existing code review workflows and deploy via the UI, the CLI or CI/CD pipelines.

Every pipeline run is logged with inputs, outputs, duration and status. Filter by success or failure, inspect logs and re-run with one click. Track resource usage, monitor worker groups and set up alerts for failures.