Welcome to droughty’s documentation!¶
#### droughty. ## adjective, drought·i·er, drought·i·est. ## dry.
Droughty helps keep your workflow ah hem dry
What is droughty?
droughty is an analytics engineering toolkit. It takes warehouse metadata and outputs semantic files.
Current tools and supported platforms are:
lookml - generates a lkml with views, explores and measures from a warehouse schema
dbt - generates a base schema from specified warehouse schemas. Includes standard testing routines
dbml - generates an ERD based on the warehouse layer of your warehouse. Includes pk, fk relationships
cube - generates a cube schema including dimensions, integrations and meassures
The purpose of this project is to automate the repetitive, dull elements of analytics engineering in the modern data stack. It turns out this also leads to cleaner projects, less human error and increases the likelihood of the basics getting done…
Documentation
Installation, configuration and usage documentation can be found on ReadTheDocs
Installation
droughty is available through pip:
pip install droughty
Dependencies
droughty uses a number of open-source projects to work properly:
[lkml](https://pypi.org/project/lkml/) - This project uses lkml as its base parser - John Temple
[ruamel.yaml](https://pypi.org/project/ruamel.yaml/) - Yaml parser - Anthon van der Neut
Pandas
Python Git
Click
Pandas GBQ
Protobuf
snowflake_connector_python
Considerations
You need to run Droughty from a git repo. It uses the Git package to control certain relative dirs Currently the cli sub-commands have an issue where all they are not mutually exclusive. This needs to be resolved but doesn’t impact usage dramatically.