Python analytics solution accelerator using composable data systems
Project description
Python analytics accelerator
A composable data system in a Python package.
This project uses:
- query UI: Ibis (Python dataframe code and/or SQL)
- query engine: DuckDB and ClickHouse (local and remote)
- storage: Delta Lake tables (local and/or cloud storage)
You can swap out your query engine(s) or storage format(s) as needed, mixing and matching as needed in a data mesh fashion. You can also use composable query UIs in other languages like dplyr
in R, though this project is Python-focused.
[!CAUTION] This accelerator is not necessarily production-ready, but could be a good starting point. For now, it is primarily intended as an educational resource.
setup
Get up and running in 4 commands.
Install:
pip install python-analytics-accelerator
[!TIP] This makes the
acc
CLI available as long as you have a Python environment with the package installed active. Typeacc
in your terminal to see available commands.
Initialize:
acc init
[!TIP] Edit the
config.py
file to the GitHub repository and PyPI package you want to analyze.
[!IMPORTANT] You must edit the
.env
to add your GitHub token or otherwise setGH_TOKEN
as an environment variable.You can use
gh auth token
to print your GitHub token if you have the GitHub CLI installed.
Ingest from external sources:
acc ingest
Run ETL:
acc run
Open dashboard:
acc dashboard
development
Clone and change into the repository:
gh repo clone lostmygithubaccount/python-analytics-accelerator
cd python-analytics-accelerator
Install:
pip install -r requirements.txt
Format your code:
ruff format .
contributing
Work in progress, contributions welcome. Consider opening an issue for discussion or reaching out in these early days.
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