PySpark File Ingestion | Read CSV, JSON & Parquet for Data Engineering (Day 3)

Welcome to Day 3 of the PySpark for Data Engineering series. In this video, we focus on one of the most important real-world Data Engineering skills — ingesting files into Spark. You’ll learn how to load CSV, JSON, and Parquet files in PySpark, understand important read options, and see why Parquet is preferred in production pipelines. --- 🎯 What you’ll learn in this video: ✔ How to read CSV files in PySpark ✔ Important CSV read options (header, delimiter, inferSchema) ✔ How to read JSON and multiline JSON files ✔ How Spark handles nested JSON ✔ How to read Parquet files ✔ Why Parquet is preferred for Silver and Gold layers ✔ Reading entire folders of files ✔ Production tips for file ingestion --- 👥 Who should watch this? • Data Engineers • Big Data beginners • Python developers learning Spark • ETL engineers • Interview preparation candidates --- 📅 Series Roadmap: ▶ Day 4 – Creating DataFrames from Python Objects ▶ Transformations & aggregations ▶ Joins ▶ Performance tuning ▶ Real-world pipelines --- 🧠 Why Parquet matters: Parquet is a columnar, compressed file format that stores schema inside the file. This makes Spark pipelines faster and more efficient. --- 🔔 Subscribe for the full PySpark for Data Engineering series 👍 Like if file ingestion is clear now 💬 Comment “Day 4” when you’re ready to create DataFrames #PySpark #ApacheSpark #DataEngineering #Parquet #BigData #PySpark #ApacheSpark #DataEngineering #BigData #SparkTutorial #ETL #Parquet #JSON #CSV #SparkFiles #LearnSpark #DataPipeline