Stage 1 · Lesson 4 of 8
ETL and ELT
stands for extract, transform, load. You pull data out of a source, clean and reshape it, and only then put it in the warehouse. Traditionally a separate tool or server did the cleaning.
ELT swaps the last two steps. You load the raw data first, into cheap storage or the warehouse, and then transform it there with . made ELT the normal choice.
Their storage is cheap and their SQL engines are powerful. Keeping the raw copy also means you can rebuild your tables when the business logic changes. dbt, which you will learn later, is an ELT tool. ETL still fits in two cases.
One is when sensitive data must be hidden (masked) before it lands anywhere. The other is when data is huge and messy, and a job is cheaper than warehouse SQL.
Common traploading raw data and never modelling it, leaving analysts to clean the same mess in every query.
A question to check yourself, an AI practice session and 1 cheat sheet entry come with the full roadmap. Unlock
Resources for this stage
Start with· follow one of these
- CourseFreeData Engineering Zoomcamp
DataTalks.Club · Beginner
Free, cohort-based or self-paced course: Module 1 covers Docker, PostgreSQL and loading real taxi data
- CourseFreeCS50's Introduction to Programming with Python
Harvard University · Beginner
If your Python is shaky, work through the lectures on functions, exceptions, file I/O and libraries first