Part 9: Spark DataFrame: String & Datetime Functions | Explained Like you are 5

Описание: 🚀 Mastering Spark DataFrame: String & Datetime Functions

Notes: https://drive.google.com/drive/folder...

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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