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.
22 entries
Techniques
8
Technique
Advanced dimensional modeling: bridge tables and the hard cases
Advanced dimensional modeling beyond the basics: bridge tables, multivalued and inferred-member dimensions, comment dimensions, and multi-timezone facts.
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Building a data warehouse: a four-phase playbook
How a data warehouse project actually gets built, across discovery, design, development, and deployment, with the Kimball vs Inmon choice treated as a concrete decision, not a debate.
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Change data capture: implementation strategies
How log-based, timestamp-based, and trigger-based CDC work in production: the snapshot-to-streaming handoff, schema-evolution failure modes, and the disciplines that keep pipelines correct.
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Data cleansing in the warehouse: where it belongs
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
Data modeling phases explained: what the conceptual, logical, and physical models each deliver, where dbt and data contracts fit, and the handoffs that decide if the model holds.
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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 pitfalls
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
12
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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Star schema
A dimensional model in which a central fact table of measurements joins directly to a ring of denormalized dimension tables, forming a star — the default analytical schema for query simplicity and speed.
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Surrogate key
A system-generated, intentionally meaningless primary key for a warehouse table — an integer sequence, hash, or UUID — that decouples the warehouse from source natural keys and enables slowly-changing-dimension history.
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