Introduction
Quickstart
👉 Trying to deploy DJ for internal use? See the guide to Deploying DJ.
👉 Trying to onboard onto DJ for data modeling? See the guide to Data Modeling.
👉 Want to contribute to DJ develpopment? See the Developers Guide
What is it?
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.
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 nodes, 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.