Part 9: Spark DataFrame: String & Datetime Functions | Explained Like you are 5
🚀 Mastering Spark DataFrame: String & Datetime Functions
Notes: https://drive.google.com/drive/folders/18l...
This comprehensive tutorial dives deep into essential Spark DataFrame functions for data engineering. Learn how to manipulate strings and handle complex datetime arithmetic using PySpark with real-world examples.
✅ What You Will Learn:
String Manipulation: Master substring for fixed-length data and split combined with explode to handle delimited strings.
Data Sanitization: Learn professional techniques for padding records with lpad/rpad and cleaning whitespace with trim, ltrim, and rtrim.
Datetime Essentials: How to use to_date and to_timestamp for data type conversion.
Time Arithmetic: Calculate date differences, add months, and find the next specific day using date_add, datediff, and add_months.
Advanced Truncation: Use trunc and date_trunc to normalize your data to the beginning of a week, month, or hour.
✅ Why This Matters:
Production Standards: These functions are critical for processing Mainframe-style fixed-length files and cleaning raw ETL data.
Scalability: Understanding native Spark functions ensures your data processing pipelines remain efficient and performant.
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