DataJunction

Introduction

DataJunction (DJ) is an open source metrics platform that allows users to define metrics and the data models behind them using SQL, serving as a semantic layer on top of a physical data warehouse. By leveraging this metadata, DJ can enable efficient retrieval of metrics data across different dimensions and filters.

DataJunction

Getting Started

To launch the DataJunction UI with a minimal DataJunction backend, start the default docker compose environment.

docker compose up

If you'd like to launch the full suite of services, including open-source implementations of the DataJunction query service and DataJunction reflection service specifications, use the demo profile.

docker compose --profile demo up

DJUI: http://localhost:3000/ DJ Swagger Docs: http://localhost:8000/docs DJQS Swagger Docs: http://localhost:8001/docs Jaeger UI: http://localhost:16686/search Jupyter Lab: http://localhost:8888

How does this work?

At its core, DJ stores metrics and their upstream abstractions as interconnected nodes. These nodes can represent a variety of elements, such as tables in a data warehouse (source nodes), SQL transformation logic (transform nodes), dimensions logic, metrics logic, and even selections of metrics, dimensions, and filters (cube nodes).

By parsing each node's SQL into an AST and through dimensional links between columns, DJ can infer a graph of dependencies between nodes, which allows it to find the appropriate join paths between nodes to generate queries for metrics.

AI Integration

DataJunction provides an MCP (Model Context Protocol) client that enables AI assistants like Claude to interact with your semantic layer.

The MCP client is part of the DataJunction Python client package.

For installation and setup, see the MCP documentation in the client package.

Documentation

For more detailed documentation, visit datajunction.io.

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