PySpark Fundamentals: Transform, Clean, and Organize JSON Data

#pyspark #pysparktutorial #textprocessing #pythonprogramming #pythontutorial #dataengineering #dataanalysis #datacleaning Link to the dataset: https://github.com/raghuveertechzone/... PySpark Data Transformation Tutorial: Clean and Prepare Nobel Prize JSON Data Time Stamps: 00:00 Intro 00:48 Read the data 01:23 Transformation-1 03:10 Transformation-2 (Drop Duplicates) 05:15 Transformation-3 (Sort the data) 06:27 Transformation-4 (Rename Columns) In this tutorial, I walk you through key PySpark data transformations using a JSON dataset on Nobel Prize laureates. Whether you’re new to PySpark or brushing up on your fundamentals, this video covers everything you need to clean, organize, and transform data efficiently in a big data environment. What’s Covered in This Video: 1. Environment Setup: • Import core PySpark libraries including SparkSession, col, concat, lit, and transform. • Create a SparkSession with custom app name and memory configurations. 2. Loading the Data: • Read the nobel_prizes.json file with multiline JSON support. • Display sample records and the schema to understand data structure. 3. Transformation 1: Column Manipulation: • Extract and manipulate array fields like laureates. • Create a new column laureates_full_name by combining individual first names using transform() and concat(). 4. Analyzing DataFrame Shape: • Count rows and columns to understand dataset size. 5. Transformation 2: Removing Duplicates: • Approach 1: Drop duplicates based on category, year, and overallMotivation. • Approach 2: Drop duplicates based on just category. • Compare the shape of the DataFrame before and after deduplication. 6. Transformation 3: Sorting Data: • Sort the DataFrame by a single column (year). • Apply multi-column sorting: year (descending) and category (ascending). 7. Transformation 4: Renaming Columns: • Rename columns directly or using selectExpr. • Rename category to Topic, overallMotivation to Motivation, and more. Why Watch This Video? • Learn how to perform essential PySpark data transformations. • Understand column operations, duplicate handling, sorting, and renaming techniques. • Build a solid foundation for working with large-scale structured and semi-structured data. Who Is This Video For? • Beginners and intermediate users exploring PySpark for big data. • Data engineers and analysts working with JSON datasets in distributed systems. • Anyone interested in building data pipelines using Spark. 🔔 Subscribe for more PySpark tutorials, big data projects, and hands-on data engineering guides! Pyspark for beginners Pyspark Tutorial Big Data Tutorial Big Data with Pyspark Distributed Data Processing with Pyspark