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Data Engineering Knowledge Base

Evergreen guides to the foundations, architecture, practices, and tools behind modern data engineering.

A left-to-right linear trail of the different Data Platforms, starting with paper, going to Relational Databases, Warehouses, Lakes, then finally Cloud Warehouses

Data Platform Evolution: From Paper Ledgers to the Lakehouse

How each generation of data technology, from relational databases to warehouses, lakes, and the lakehouse, solved the limits of the one before, and why they all still coexist today.

Reviewed Aug 2026

Split Panel image with OLTP on the left and OLAP on the right/ OLTP shows an App and Database with many arrows connecting between (for thousands of tiny reads & writes), whereas OLAP shows one arrow between a Warehouse and Report (one heavy question)

Foundations

OLTP vs OLAP: The two jobs every Database is asked to do

One workload runs the business, the other understands it. The split that explains why data warehouses, pipelines, and half of data engineering exist.

Reviewed Aug 2026

A diagram breaking down the lifecycle of Data Engineering with clean examples of sources on the left (Databases, APIs, etc), collection, storage and serving in the middle, and consumption on the right (BI, ML, etc)

Foundations

What is Data Engineering?

The invisible work behind every dashboard, forecast, and AI feature. What data engineers actually do, why it matters commercially, and how the role differs from data science.

Reviewed Aug 2026

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