Handle Corrupted Data in PySpark | Read Modes Explained
In this video, we’ll explore how to detect and handle corrupted data in Apache Spark using PySpark. You’ll learn what corrupted data looks like, the available read modes in Spark, and how to manage bad records effectively with practical hands-on examples in Databricks.
All code and data files are available on the below path:
https://github.com/databeli/pyspark_c...
PowerPoint Presentaion useed in the complete playlist(27 slides)
https://topmate.io/narender_kumar_91/...
What you’ll learn:
What is corrupted data in Spark (examples for JSON and CSV files)
Understanding Spark read modes:
Permissive Mode: Replace invalid data with null and capture in _corrupt_record
DropMalformed Mode: Silently skip corrupted records
FailFast Mode: Stop processing on corrupted data
Using Bad Records Path to store corrupted records centrally
How to inspect and analyze bad records in Databricks
Understanding recursive file lookup and how Spark logs bad record details
Comparing both methods: read modes vs bad records path
Best practices for handling corrupted data in PySpark
By the end of this video, you’ll know how to handle bad or malformed records gracefully using Spark’s built-in options and maintain cleaner, reliable datasets in production pipelines.
#pyspark #pysparktutorial #corrupteddata #databricks