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pz is a command-line engine that moves data in batches. You describe a project as SQL files plus one YAML file of connections, and pz compiles them into a dependency graph that DuckDB executes. This page is the map of the documentation. Pick the row that matches what you need.

If you want to Read
Install the tool Install pz
See a project run in ten minutes Quickstart
Build a real pipeline step by step Tutorial
Learn the vocabulary Key concepts

The concept pages explain one idea each, in the order you meet them when writing a project.

  • Project layout: the files in a project and what each one does.
  • Connections and entities: the one YAML declaration that describes a place and the things you read from or write to it.
  • Pipelines: SQL files that read with source() and ref() and load with sink().
  • Checks: assertions that run inside the graph and gate what gets written.
  • Incremental loads: watermarks, bounded windows, change data capture, and merge.
  • Selecting nodes: running part of a project with --select.
  • How a run works: compile, plan, execute, and the artifacts a run leaves behind.
  • Delivery guarantees: what pz promises when something fails halfway.
  • Validation and errors: the five validation tiers and how to read an error.
  • State: the .pz/ directory, watermarks, and remote state backends.
  • Schema contracts: column contracts, drift detection, and schema policy.
  • Connectors: builtin and third-party connectors, restore, and lock files.

The how-to guides are grouped by job: ingest, reliability, production, AI agents, and extending pz. Each guide states its goal, prerequisites, steps, and how to verify the result.

The Internals section explains the engine for people who change it: architecture, the two-tier data plane, execution and resume internals, the connector ABI, a code tour, and the contributing guide.

Data Engineering Introduction is a ten-part introduction to pipelines in general, with the last article showing how pz maps onto each idea.