Welcome to PyQuery HQ — the central hub for PyQuery, a Python-native, Power Query–style ETL platform built for people who want clean, modular data transformation without giving up code.

PyQuery is designed to make data pipelines:

• Step-based and composable
• Reproducible and auditable
• Fast enough to feel unfair
• Native to the Python ecosystem

Think Power Query workflows, but engineered for modern Python, serious data volumes, and long-term maintainability.

PyQuery HQ hosts the core ecosystem around PyQuery, including:

• The ETL engine and execution runtime
• Connectors and pipeline steps
• UI and CLI tooling
• Exporters, plugins, and integrations
• Docs, examples, and reference implementations

This organization is the source of truth for how PyQuery evolves.

PyQuery is created and maintained by Shan.TK (Sudharshan TK).

Shan is a data & analytics engineer with a strong focus on:

• Audit and analytics tooling
• Automation-first design
• Reproducible, explainable data workflows
• Bridging the gap between low-code analytics and serious engineering

PyQuery was born out of real-world frustration with brittle pipelines, opaque transformations, and tools that don’t scale cleanly beyond demos.

This project reflects a simple belief:

Data transformation should be structured, transparent, and developer-first.

PyQuery is being built with an open mindset.

If you care about:

• Data engineering and analytics tooling
• Clean architecture and modular systems
• Developer experience over buzzwords

—you’ll fit right in.

Contribution guidelines and roadmap will live in the relevant repositories as the ecosystem opens up.

• 📘 Documentation: coming soon
• 🧪 Examples & demos: coming soon
• 🛠️ CLI & tooling guides: coming soon

(Yes, we’re moving fast. Yes, this will fill up.)

• Pipelines are designed before dashboards
• Reproducibility > vibes
• Clean abstractions beat quick hacks every time

Built with intent.
PyQuery — modular ETL, Python-native.
_{by Shan.TK}