ETL vs ELT

Concepts covered: paEltVsEtl

The two acronyms ETL and ELT differ by a single letter, but the architectural implications are large. ETL extracts data from sources, transforms it on a separate compute layer, and loads the transformed result into the destination. ELT extracts the data, loads it into the destination warehouse first, and runs the transforms inside that warehouse. The order is the entire difference, and that order changes which system bears the cost of the transform work. Why ETL Was the Default Before cloud warehouses, storing data was expensive and querying it was even more expensive. A data warehouse like Teradata or Oracle charged for every gigabyte and every CPU cycle. The economics forced data engineering teams to transform data outside the warehouse, on cheaper general-purpose hardware, and load only

About This Interactive Section

This section is part of the What a Data Pipeline Is: Intermediate lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.