Topic
Dimensional Modeling
Star schemas, fact and dimension tables, grain, surrogate keys, slowly changing dimensions, and the design choices that determine whether a model holds up in production.
20 entries
Techniques
8
Technique
Advanced dimensional modeling: bridge tables, inferred members, multi-timezone, and the awkward cases
How to model the dimensional cases the textbook example never quite covers: multivalued dimensions and bridge tables, inferred members for late-arriving dimensions, free-text comments, and facts that span multiple time zones.
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Building a data warehouse: a four-phase practitioner's playbook
How warehouse projects actually get built, organized as discovery, design, development, and deployment, with the Kimball-versus-Inmon design choice treated as a concrete decision rather than an academic debate.
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Change data capture: implementation strategies
How log-based, timestamp-based, and trigger-based change data capture actually work in production, including the initial snapshot handoff, schema evolution failure modes, and the operational disciplines that keep CDC pipelines correct.
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Data cleansing in the warehouse: where it belongs and what it does
Where data cleansing sits in a modern warehouse load: the staging-to-curated boundary, the rule categories that catch real defects, the test-at-the-transform-layer pattern, and the observability that catches the drift the rules miss.
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Data modeling phases: conceptual, logical, and physical
How conceptual, logical, and physical data models actually divide warehouse design work in 2026, including where data contracts and dbt fit, and the handoffs that determine whether the model survives production.
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Normalization and denormalization in data warehousing
Normalization vs denormalization for analytical workloads: where 3NF still belongs in a 2026 warehouse, why columnar engines have made denormalization the default for query layers, and how to think about the trade-off layer by layer.
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Slowly changing dimensions: implementation strategies
How SCD Type 1, 2, 3, and the hybrid types actually work in a production warehouse, including active row identification, fact loading under Type 2, and the edge cases that bite teams in practice.
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Surrogate key management: generation, lookup, and the cases that bite
How to generate and manage surrogate keys in a 2026 cloud warehouse: integer sequences, hash-based deterministic keys, UUID v7, the fact-loading lookup under Type 2 SCD, and the edge cases that produce silent errors.
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Glossary
10
Glossary
Bridge table
A dimensional-modeling structure that resolves many-to-many relationships between a fact table and a dimension by carrying one row per (group, member) pair.
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Conformed dimension
A dimension used identically across multiple fact tables or data marts. The mechanism that lets independent marts roll up into an integrated enterprise warehouse without metric definitions drifting apart.
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Degenerate dimension
A dimensional attribute that lives on the fact table directly, without a separate dimension table. Used for high-cardinality transactional identifiers that have no descriptive attributes worth grouping by.
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Factless fact table
A fact table that records the occurrence of an event without any additive numeric measures. The right shape for events whose analytical value is the event itself, not a quantity attached to it.
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Grain (dimensional modeling)
The precise definition of what one row in a fact table represents. The most consequential design decision in a dimensional model, and the one most frequently skipped.
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Inferred member
A placeholder dimension row inserted at fact-load time when the fact references a business key that does not yet exist in the dimension, with a flag that flips when the real attributes arrive.
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Multivalued dimension
A dimension that has multiple values per fact row, breaking the one-foreign-key-per-dimension assumption of the canonical star schema and requiring a bridge table to resolve.
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Referential integrity
The property that every foreign key value in a child table actually exists in the parent table it references. In a data warehouse, the question of where to enforce this property, in the database engine, in the transformation layer, or not at all, is a long-running design debate that the move to cloud platforms has decisively reshaped.
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Semantic layer
A modeling abstraction between physical warehouse tables and BI tools that defines business entities, metrics, and dimensions once, so downstream consumers query consistent definitions rather than rebuilding them per report.
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Slowly changing dimension
A dimension whose attribute values change over time at a rate slower than fact table growth, requiring explicit strategies to preserve or overwrite history.
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