Learn how to create a Data Frame in PySpark from JSON and Parquet File.

JSON vs. Parquet: Which file format is best for your data workflow? 🤔 In this video, we dive deep into the world of data handling with PySpark! From loading and processing JSON files, including nested and multi-file structures, to working with the highly efficient Parquet format, we’ve got you covered.
Learn how to read, explore, and extract insights from these file formats with practical coding examples. We also showcase essential PySpark DataFrame operations like counting rows, identifying columns, and finding distinct records to take your data analysis to the next level.
Whether you're a beginner or a seasoned data professional, this tutorial will give you the tools and confidence to work with massive datasets efficiently. Don’t miss it!
📌 What you’ll learn:
• How to read and process JSON files in PySpark
• Working with nested JSON using the explode function
• Why Apache Parquet is perfect for big data
• Essential DataFrame operations in PySpark
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💬 Share your thoughts in the comments—What’s your favorite file format for big data and why?