Tag
dimensional-modeling
- Aug 4, 2026 Evolving a Dimensional Model Without Rewriting Its History Which schema changes are additive, which silently change a measure's meaning, and how to refine a grain or retire a column while old and new consumers coexist. data engineering
- Jun 10, 2026 Choosing Between SCD Types 1, 2, 3 and 6 The cost of overwriting an attribute, the cost of versioning it, and the cost of keeping a previous value in a column, set against the questions each type can and cannot answer. data engineering
- Feb 9, 2026 Mini-Dimensions and Junk Dimensions: Narrowing a Wide Fact Table How to keep fast-changing attributes out of a versioned dimension, and how to collapse a handful of low-cardinality flags into one key instead of a dozen foreign keys. data engineering
- Jan 2, 2026 Conformed Dimensions and the Bus Matrix Why two fact tables can only be compared when they share identical dimension keys and members, and how a bus matrix plans those dimensions before any table is built. data engineering
- Sep 20, 2025 Accumulating Snapshot Fact Tables: One Row Per Process, Updated in Place How a fact row can be rewritten as an order moves through picking, shipping and delivery, which measures stay additive, and why the row must be created before the process finishes. data engineering
- Oct 31, 2024 Star Schemas and the Cost of Normalizing a Dimension What denormalizing a dimension into one wide table buys at query time, what it costs in update work, and why snowflaking usually loses on columnar storage. data engineering
- Oct 10, 2024 Grain: What One Row of a Fact Table Means Why the grain declaration precedes every column choice, how it fixes which measures can be summed, and what a join at the wrong level does to a count. data engineering