Tag archive
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.
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.
Fact tables, dimension tables, grain, star schemas, and slowly changing dimensions — all explained with a friendly neighborhood cafe. Code samples in PostgreSQL.
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.
Building a data warehouse is about far more than simply storing data. As enterprise data volumes...
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.
Every large, established company seems to have one: a big, powerful data system
Teams keep forcing their warehouse to do integration work it was never designed for. The result is...
This article targets junior data engineers and data analysts who are familiar with: ELT...
Algoscale is a trusted technology consulting company specialising in data warehouse, cloud analytics,...