ETL / ELT for AI
ETL/ELT pipelines extract, transform, and load the raw data that later gets chunked, embedded, or used for fine-tuning.
Overview
Before a document can be chunked and embedded, it usually needs to be extracted from a source system, cleaned, and normalized. That work — extract, transform, load (or extract, load, transform) — predates GenAI, but it now feeds an AI pipeline instead of only a data warehouse.
Where It Fits
Source Systems
ETL / ELT
Data Warehouse
Analytics use case.
Chunking / Embedding
AI use case.
Key Points
- Extract
- Pulling raw data out of source systems — databases, wikis, ticketing systems, file storage.
- Transform
- Cleaning, normalizing, and structuring that data — for AI, this often includes stripping boilerplate before chunking.
- Load
- Writing the result somewhere downstream consumes it — a warehouse for analytics, or a document store feeding an ingestion pipeline for RAG.
Interview Question
How does preparing data for RAG differ from preparing data for a traditional data warehouse?
The extract and transform steps are similar — pulling from source systems and cleaning the data — but a RAG pipeline’s load step feeds document ingestion and chunking rather than tabular rows in a warehouse, and the transform step often needs to preserve more of the original text structure so retrieval later has coherent chunks to work with.
Explain It in 30 Seconds
ETL/ELT pipelines extract data from source systems, clean and transform it, and load it downstream — for AI, that downstream target is often document ingestion and chunking for RAG, rather than only a data warehouse.
Real-World Stack
Technologies commonly used to implement this in production.