Data Transformation Guide: Map, Filter, Aggregate, and Reshape Records
Transform data records using map, filter, reduce, pivot, and join patterns. Apply these in Python, SQL, dbt, and pandas for any pipeline stage.
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Tags: data, transformation, engineering
Data Transformation Guide: Map, Filter, Aggregate, and Reshape Records Data transformation is the middle layer of every data pipeline. Raw source data almost never matches what downstream consumers need — columns are named wrong, types are inconsistent, records need to be grouped, and schemas need restructuring. This guide covers the five core transformation patterns every data engineer must know, with code examples in Python and SQL. Pattern 1: Mapping (Field Rename, Type Cast, Derived Fields) Mapping transforms individual fields — renaming them to match the target schema, casting types, or deriving new fields from existing ones. Field Renaming Source systems have their own naming conventions. Your warehouse has yours. In SQL (useful for dbt models): Type Casting Source data is often…
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