How do we clean raw product SKU codes using native regex and trimming functions instead of custom Python loops?
Chaining native upper(), trim(), and regexp_replace() functions to execute entirely in Catalyst bytecode.
Production data cleansing pipelines operating over billions of e-commerce records.
from pyspark.sql.functions import col, regexp_replace, trim, upper
clean_df = raw_df.withColumn(
"clean_sku",
upper(trim(regexp_replace(col("raw_sku"), "[^a-zA-Z0-9]", "")))
)Practice typing production-grade PySpark code for Replacing UDFs with High-Performance Built-ins.