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.
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🎯 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
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👥 Who should watch this?
• Data Engineers
• Big Data beginners
• Python developers learning Spark
• ETL engineers
• Interview preparation candidates
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📅 Series Roadmap:
▶ Day 4 – Creating DataFrames from Python Objects
▶ Transformations & aggregations
▶ Joins
▶ Performance tuning
▶ Real-world pipelines
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🧠 Why Parquet matters:
Parquet is a columnar, compressed file format that stores schema inside the file.
This makes Spark pipelines faster and more efficient.
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💬 Comment “Day 4” when you’re ready to create DataFrames
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