A data warehouse is built for questions, not transactions. Facts and dimensions in plain words, why the star schema reads the way people talk, what a date dimension is for, and when you do not need one.
Tabular Models for the SQL Developer
Tabular models for the SQL developer add relationships, measures, security, and reusable semantics above SQL tables; learn when a view is enough.
SQL SERVER – SSAS – Multidimensional Space Terms and Explanation
At a Tech Ed On Road session I was asked to explain multidimensional space in simple words. Here are dimension, member, value, attribute and size.
SSAS Cube Performance: Diagnosing a Sudden Slowdown
SSAS cube performance diagnosis starts with the exact slow request. Follow storage, MDX calculations, cache, processing, and resource evidence before choosing a fix.
Late-Arriving Dimension Rows: Inferred Members in a Data Warehouse
Handle a late-arriving dimension with inferred members, preserve fact keys, fill attributes later, and report unresolved placeholders.
Slowly Changing Dimensions in Plain T-SQL
Slowly changing dimensions need explicit Type 1 and Type 2 rules; use T-SQL updates and inserts with tests for history and overlap.
Getting SQL Server Data Into Power BI
Power BI can read SQL Server through Import or DirectQuery; stable views, folding, and workload checks keep the database responsive.