Why 80% of Data Engineering is Cleaning (and How to Do It Right)

Data Cleaning & Denoising: The "Battlefield" of Data Engineering 🧹 It is an industry consensus that data engineers spend 60% to 80% of their time on data cleaning. Why? Because raw data is messy, and "garbage in, garbage out" is the absolute truth in data science. In this post, based on the data_engineering_book , we’ll deconstruct the logic of industrial-grade data cleaning—moving from "just fixing bugs" to "building robust cleaning pipelines." 1. Where Does the "Noise" Hide? According to the Data Engineering Book , data quality is the prerequisite for data value. Noise typically falls into 5 categories: Noise Type Symptoms Business Impact Missing Values Null addresses, missing age fields Failed deliveries, incomplete user segments Outliers $1M orders (avg is $100), 1000°C sensors Flawed sales forecasts, cost miscalculations Duplicates Double-submitted forms, sync errors Inflated user counts, duplicate revenue Inconsistency "2024-05-01" vs "05/01/24" Aggregation failures, broken time-