#datawarehouse

Martin Tuncaydin explores how travel companies can build enterprise-grade analytics infrastructure with cloud warehouses, transformation frameworks, and m…

As enterprises accelerate their digital transformation, data platforms are evolving from traditional...

Part 4 of the Kimball series — bridge tables for genuine many-to-many relationships, the weighting-factor pattern for splitting a dollar amount across multiple codes without inventing money, and where that pattern actually breaks down.

Fact tables, dimension tables, grain, star schemas, and slowly changing dimensions — all explained with a friendly neighborhood cafe. Code samples in PostgreSQL.

Part 3 of the Kimball series — accumulating snapshots for real multi-stage processes, the grain fight partial shipments force, out-of-order webhook handling, and the semi-additive measure problem MRR only hinted at.

Building a data warehouse is about far more than simply storing data. As enterprise data volumes...

Modernising legacy data warehouses can allow you to reduce complexity, unlock value, and lower costs...

Kirish: Big Data va “ma’lumotlarni boshqarish” nimani anglatadi Big Data sharoitida ma’lumot hajmi...

title: Why I left Warehouse out of our Fabric deployment scope published: true tags: microsoftfabric,...

A data warehouse vs data lake decision is rarely either/or. See what each stores, what jobs each fits, the lakehouse middle ground and how to choose.

Part 5 (final) of the Kimball series — a deal with three stakeholders, three defensible grains, and three genuinely different numbers, none of them wrong. Conformed dimensions, and why the answer isn't picking one.

Every large, established company seems to have one: a big, powerful data system