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

Want to master ETL pipelines with PySpark? 🚀 In this step-by-step tutorial, we’ll walk through how to extract, transform, and load (ETL) data like a pro using Apache Spark. Whether you’re a beginner or an aspiring data engineer, this video will give you the hands-on knowledge you need to build scalable data workflows.

🔑 What you’ll learn:

What ETL is and why it matters in data engineering

Setting up PySpark for big data processing

Building an end-to-end ETL pipeline

Best practices for efficiency and scalability

How to prepare your ETL skills for real-world projects

🛠️ Build This LIVE:
We’ll create an end-to-end pipeline that:
🔹 Extracts data from multiple sources
🔹 Transforms data using PySpark DataFrames
🔹 Loads results into a data warehouse (Delta Lake)
🔹 Handles errors and ensures data quality

"Have you ever wondered how companies like Netflix, Uber, or Amazon process massive amounts of data in real time? 🤔 The secret lies in ETL pipelines—and today, I’m going to show you exactly how to build one using PySpark.

By the end of this video, you won’t just know what ETL is—you’ll be able to design, code, and run a real ETL pipeline that can handle big data at scale. Stick around, because I’ll also share some pro tips to make your pipelines faster, cleaner, and production-ready. Let’s dive in!"

👉 Don’t forget to like, comment, and subscribe for more data engineering, PySpark, and big data tutorials!

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