You load data into Snowflake or de‑bucket from S3. What kind of processing do you do using PySpark?

After loading raw files from S3 into Snowflake staging tables, I use PySpark for heavy transformations that are not efficient in SQL. Typical steps include reading the data into a Spark DataFrame, performing schema inference or applying a predefined schema, cleaning (null handling, type casting), and enriching with joins to reference tables. I use window functions for ranking, UDFs for custom logic, and Spark SQL for complex aggregations. Performance tuning involves partitioning by key columns, caching intermediate results, and using broadcast joins for small tables. Once the transformed data is ready, I write it back to Snowflake using the Snowflake connector, ensuring data is partitioned by date for efficient querying. This hybrid approach leverages Snowflake’s storage and compute for analytics while using Spark’s distributed processing for data preparation.