Data architecture in plain terms: databases vs warehouses vs lakes, ETL pipelines, choosing a setup for your size, and keeping data systems healthy.
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What Data Systems Does
Maps data architecture in plain terms — databases, warehouses, lakes, and the pipelines between them — so readers can see how organizations keep information organized.
The workhorses of daily operations — rows, tables, and transactions explained.
Where history accumulates for analysis — and how that differs from a database.
Raw everything, structured later — when a lake helps and when it just gets murky.
The extract-transform-load plumbing that moves data between all of the above.
A right-sized setup for a 5-person shop versus a 5,000-person company.
Monitoring, documentation, and cleanup that keep an architecture trustworthy.
How Data Systems Works
Know what databases, warehouses, and lakes each do before drawing arrows.
Understand the pipelines that copy and reshape data between systems.
Build the smallest architecture that answers today's questions reliably.
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A database runs daily operations, a warehouse organizes history for analysis, and a lake stores raw everything for later. Most small teams only need the first.
Extract, transform, load: copy data out of one system, reshape it, and land it in another. It is the plumbing between the boxes on every architecture diagram.
Usually not at first. A well-kept database and a reporting tool go a long way; a warehouse earns its keep when history and sources multiply.
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