Skip to main content

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.

Metadata

Release files for datajunction-server 0.0.264

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for datajunction-server 0.0.264
File Size Uploaded Provenance
datajunction_server-0.0.264.tar.gz 9.7 kB With provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for datajunction-server 0.0.264
File Interpreter ABI Platform
datajunction_server-0.0.264-py3-none-any.whl Python 3 none any Details

Total release size: 1.4 MB

Release files / datajunction_server-0.0.264.tar.gz

Download URL datajunction_server-0.0.264.tar.gz
Size 9.7 kB
Tags Source
SHA-256 checksum
How to use checksums
420fea86e13c2823e6f18621a68510d707feb70247c1609681a1720f18c416da
BLAKE2b-256 checksum
How to use checksums
46d40cd1aa64d2c4cd2a6b0ef66b00d4dc55ddcf47db7c01709a63f3aa78f7df
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 21, 2026.

Transparency log

Release files / datajunction_server-0.0.264-py3-none-any.whl

Download URL datajunction_server-0.0.264-py3-none-any.whl
Size 1.4 MB
Tags Python 3
SHA-256 checksum
How to use checksums
0e3cbd09e7c658cd895a3dd5dddb0be9950258023b0d86e328ea66c2e97dcda2
BLAKE2b-256 checksum
How to use checksums
d4f899435b0e15eef128059ac36b5a0f5e7a5cb2b124914de47c56937f5880f6
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 21, 2026.

Transparency log

Security for datajunction-server 0.0.264

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Full provenance

All 2 files in this release have been uploaded with provenance metadata.

Select from 2 files

Attestation for datajunction_server-0.0.264.tar.gz

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 21, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

Release history Release notifications | RSS feed

This release

0.0.264 This release

2 release files Full provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page