Topic — 11 essays
Data Modeling
How to shape data on purpose: dimensional modeling, star schemas, fact tables, keys, and slowly changing dimensions — the structural decisions that make analytics consistent.
Field Notes
One Big Table vs the Star Schema: The Real Trade-off
One Big Table (OBT) denormalizes everything into a single wide table; the star schema keeps facts and dimensions separate. Here's what each actually costs, and why t...
02Field Notes
Data Vault vs Dimensional Modeling: Which Belongs Where
Data Vault and dimensional modeling aren't rivals — they solve different problems at different layers. Here's what Data Vault actually is, what it costs, and when it...
03Field Notes
Fact Table Types: Transaction, Periodic Snapshot, and Accumulating
There are three kinds of fact table, distinguished by what one row represents over time: transaction, periodic snapshot, and accumulating snapshot. Here's how each w...
04Field Notes
Slowly Changing Dimensions, Explained Without the Jargon
Slowly changing dimensions answer one question: when a dimension attribute changes, do you overwrite history or preserve it? Here are SCD Types 1, 2, and 3, and exac...
05Field Notes
Factless Fact Tables, Explained
A factless fact table records that an event happened — or could have — without any numeric measure. Here's why a fact table with no facts is useful, the two types, a...
06Field Notes
What Are Conformed Dimensions, and Why Do They Matter?
A conformed dimension is a single dimension shared identically across multiple fact tables, so different business processes can be compared on the same terms. Here's...
07Field Notes
The Date Dimension: How to Build One and Why You Need It
A date dimension is a table with one row per calendar day, pre-loaded with every attribute of that day. Here's why every warehouse needs one, what columns to include...
08Field Notes
Star Schema vs Snowflake Schema: Which to Use and When
Star schema vs snowflake schema comes down to one decision — whether to normalize your dimensions. Here's the trade-off, and why the star usually wins in a modern wa...
09Field Notes
Fact Table vs Dimension Table: The Core Distinction
A fact table stores the measurements — the numbers you analyze. A dimension table stores the context you analyze them by. Here's the distinction every dimensional mo...
10Field Notes
A Field Guide to Dimensional Modeling
Facts, dimensions, and grain — the three ideas that quietly run most analytics, explained without the dogma.
11Field Notes
Surrogate Keys vs Natural Keys: A Practical Rule
Surrogate key vs natural key is a decision every data model faces. The practical rule: use surrogate keys for dimensions, keep the natural key as an attribute, and h...