Data Engineering
ETL (Extract, Transform, Load)
The 3-step process of pulling data from sources, cleaning/reshaping it, and loading it into a target system.
Definition
A data integration pattern that extracts data from source systems, transforms it (cleaning, mapping, aggregating), and loads it into a destination like a data warehouse. Modern variants include ELT (load then transform).
Why it matters
The unglamorous but essential plumbing that makes every dashboard, report, and AI model possible.
Related terms in Data Engineering
Batch Processing
Processing a large group of data all at once on a schedule, rather than one piece at a time in real-time.
Chunking Strategies
Chopping up long documents into small, bite-sized pieces so an AI can search and read them easily.
Data Augmentation
Creating fake but realistic training examples (like flipping or rotating images) to give the AI more data to learn from.
Data Labeling
The human work of tagging data with correct answers so an AI can learn from it, like marking photos as "cat" or "dog."
From vocabulary to outcomes
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