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.
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.
While all the functionality above currently works, DJ is still not ready for production use. Only a very small number of functions are supported, and we are still working towards a 0.1 release. If you are interested in helping take a look at the issues marked with the "good first issue" label <https://github.com/DataJunction/dj/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22>_.