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Python analytics solution accelerator using composable data systems

Project description

Python analytics accelerator

A composable data system in a Python package.

layers

This project uses:

  1. query UI: Ibis (Python dataframe code and/or SQL)
  2. query engine: DuckDB and ClickHouse (local and remote)
  3. 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. Type acc 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 set GH_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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