Скачать или смотреть Build a SCALABLE ETL Pipeline with PySpark: From Scratch to Production! (Full Walkthrough)

Want to learn how real data engineers build scalable, production-grade ETL pipelines?
In this hands-on, step-by-step tutorial, we take you from zero to fully functional ETL pipeline using PySpark—one of the most powerful tools in big data processing.

Whether you're preparing for a data engineering job, working on enterprise pipelines, or leveling up your Spark skills, this video gives you the foundations + real-world patterns you need to succeed.

🚀 What You’ll Learn in This Video:

✔ PySpark fundamentals for ETL (fast intro)
✔ Designing a robust ETL architecture
✔ Extracting data from multiple sources (CSV, JSON, APIs, databases)
✔ Transformations using Spark DataFrames
✔ Cleaning, filtering, aggregating & enriching large datasets
✔ Loading data into data lakes and data warehouses
✔ Partitioning, optimization & schema design
✔ Logging, error handling, and production best practices

🔥 Real-World Concepts Covered:

Schema evolution & validation

Performance optimization strategies

Batch vs. streaming pipeline patterns

How to structure ETL code for scale & maintainability

This isn’t just a coding tutorial—it’s a practical engineering guide that mirrors what top tech companies do in production.

👤 Perfect For:

Aspiring and junior data engineers

Python developers learning Spark

Analytics & ML engineers who process large datasets

Students preparing for DE interviews

Anyone building cloud-scale data pipelines

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