ETL Pipeline for Data Engineering: A Beginner's Guide to Extract, Transform, and Load

Short summary

An ETL pipeline extracts raw data from multiple sources, transforms it to fix inconsistencies (duplicates, nulls, format mismatches), and loads it into a warehouse for analytics. This tutorial covers the three-stage process, unification patterns that create shared customer/product keys across sources, orchestration tools like Airflow and dbt, and includes worked SQL examples. Use this as interview prep or a practical guide to building your first pipeline.

• •ETL turns messy raw data into clean, trusted data through Extract-Transform-Load stages
• •Transform stage handles deduplication, null resolution, format standardization, and key unification
• •Orchestration tools (Airflow, dbt, Spark, AWS Glue) automate pipelines; includes worked SQL examples and interview questions

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