dltHub

Target users

Data engineers, mid-level engineers, and staff data engineers who need to build, prototype, and deploy data pipelines quickly. The platform is designed to unblock teams by letting less senior engineers spin up prototypes and validate schemas without senior oversight. It also serves AI agents directly, enabling agentic data workflows.

How to use dltHub?

1. Install dlt: Run pip install dlt to get the open-source library.
2. Describe your pipeline: Prompt the agent in natural language (e.g., "Build a pipeline that loads CRM contacts and deals into my warehouse using dlt").
3. Agent creates the pipeline: The agent scaffolds source, destination, schema, incremental loading, and tests in pure Python.
4. Deploy with dltHub Pro: Run pip install dlt[hub] and use a single command to deploy to production with scheduling, alerting, and observability.
5. Verify results: Browse loaded data, inspect schemas, and validate results in the interactive notebook workspace.

Effect review

The real-world feedback from Tasman Analytics highlights a key unlock: mid-level engineers can independently prototype, inspect raw data in DuckDB, and validate schemas without pulling in senior staff. This "prototype, inspect, fix, re-run" loop is described as the platform's true value. The agentic workflows go beyond simple autocomplete, providing guided sequences with guardrails that agents cannot skip. For teams building data pipelines at scale, dltHub delivers a practical, agent-first approach that reduces dependency on senior engineers while maintaining production-grade reliability.