Data Transformation Guide: Map, Filter, Aggregate
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. --- Why Transformations Matter More Than Extracts Extraction is mostly plumbing — connect to a source, paginate through results, land data somewhere. Transformation is where correctness is decided. A bad extract produces noisy data; a bad transformation produces wrong data that looks right. Silent data corruption is far more dangerous than a pipeline crash.…
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