Data Engineering
Data Pipeline
The automated plumbing that moves data from where it's collected to where it's analyzed and used.
Definition
An automated set of processes that extract, transform, and load (ETL) data from source systems to target destinations. Includes data validation, cleaning, enrichment, and delivery to analytics or ML systems.
Why it matters
Clean, reliable data pipelines are the foundation everything else builds on, broken pipes mean broken AI.
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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