Code and Cheat Sheets For "Python Data Engineering Resources" Book

Welcome to digital downloads for "Python Data Engineering Resources" book!

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What's in here?

30+ Python projects examples covering a wide range of data engineering topics:

• ORMs (SQLAlchemy, Django ORM, Peewee)
• Data validation (Pydantic, Marshmallow, Cerberus)
• Database migrations (Alembic, Django migrations, Flask-migrate)
• Data wrangling (Pandas, Dask, NumPy)
• ETL frameworks (dbt, PySpark, dlt)
• Orchestration tools (Airflow, Prefect, Dagster)
• Data visualization (Matplotlib, Seaborn, Plotly)
• Machine learning (Scikit-learn, TensorFlow, PyTorch)
• API development (FastAPI, Django REST, Flask)
• Stream processing (Kafka, Flink)

Each section is like a mini-workshop, helping you build practical skills in data engineering. Remember, these aren't production-ready systems. They're starting points for you to learn and experiment with. Take your time, try things out, and have fun building your data engineering skills with Python.

4 Handy cheat sheets as PDF documents, containing all the URL links from the book.

They're designed to be your quick reference guides when you're knee-deep in code:

1. Python Tools and Frameworks (101 items): This is your go-to list for Python data engineering tools. It covers everything from ORMs and data wrangling libraries to machine learning frameworks and cloud services.
2. Free Datasets (75 sources): Need data to practice with? This sheet's got you covered. It lists 75 free datasets from various fields like economics, health, climate, and more. It's perfect for when you want to test your skills on real-world data without spending a dime.
3. Free APIs (56 options): From weather data to cryptocurrencies, from cat facts to government statistics, there's an API for almost everything. It's a treasure trove for building interesting projects or adding external data to your applications.
4. Learning Resources: This sheet is all about leveling up your skills. It's split into four sections:
   • Tutorials: Hands-on guides to help you learn by doing.
   • Courses: Structured learning paths from reputable platforms.
   • Books: In-depth resources for when you want to dive deep.
   • Communities and Forums: Places to ask questions, share knowledge, and connect with other data engineers.
   • Blogs: Stay up-to-date with the latest in data engineering.