ELT: Load Raw Data, Transform in Place
ELT flips the data pipeline: load raw data first, then use the data warehouse's own power to transform it. It's used in ML feature pipelines. The footgun is assuming it's ETL; with ELT, the transformation logic is coupled to the warehouse's SQL engine.
ELT (Extract, Load, Transform) loads raw data directly into a data warehouse before any changes are made. The transformation happens last, in-place, using the warehouse's own compute, typically SQL. This pattern is common for building ML feature pipelines in modern cloud data warehouses. The main footgun is confusing it with traditional ETL; with ELT, your transformation logic is tied to the warehouse's capabilities, unlike ETL which uses a separate engine like Spark.
Read the original → hopsworks.ai
- #elt
- #data engineering
- #mlops
- #data warehouse
Get five bites like this every day.
Tezvyn delivers a daily feed of 60-second tech bites with quizzes to lock in what you learn.