Data Engineering Workspace

This repository consolidates practical Data Engineering patterns, covering SQL analytics, data pipelines, orchestration, and analytical modeling.

The focus is on reproducible, production-oriented approaches using synthetic data to represent real enterprise scenarios such as ERP systems, e-commerce platforms, and system integrations.

This workspace is designed to document decision-making, trade-offs, and implementation patterns commonly encountered in Data Engineering roles.

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Environment

Local development environment configured with:

• Windows + WSL2
• Docker & Docker Compose
• PostgreSQL (containerized)
• VS Code
• DBeaver

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Repository Structure

• 00-labs — isolated technical labs and tooling experiments
• 01-sql-foundations — core SQL analytical patterns and performance considerations
• 02-etl-python — ETL pipelines implemented in Python
• 03-orchestration — workflow orchestration and scheduling concepts
• 04-analytics-modeling — analytical and dimensional modeling patterns
• shared — reusable utilities, SQL templates, and documentation
• archive — legacy files and reference material

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Notes

All datasets are synthetic and intentionally small to allow full control over data relationships, query behavior, and performance characteristics.