Model Materialization
The same model can be a view, a rebuilt table, or an incremental load. The choice decides build cost, query speed, and what re-running it actually does.
Data Modeling is the process of creating a conceptual, logical, or physical representation of data structures, relationships, and rules within a system. It helps define how data is stored, accessed, and managed. Common types include conceptual models (high-level business view), logical models (detailed structure without physical implementation), and physical models (specific database designs). Data modeling is essential for database design, data warehousing, software development, and ensuring data integrity and consistency.
The same model can be a view, a rebuilt table, or an incremental load. The choice decides build cost, query speed, and what re-running it actually does.
Denormalization copies facts into more places so reads get simpler or faster. The price: every copy must be kept consistent by something other than the schema.
Operational and analytical models face different pressures for writes, scans, meaning, and history. See what each protects and how to connect them.
A data model has three levels — what exists, how it is represented, and how it is stored. Each is decided by different people and changes for different reasons.
“Customer” means one thing to billing and another to support. A bounded context is where a term has one meaning; at its edges, meaning must be translated.
How row meaning, identity, dependencies, aggregation, and time shape operational and analytical data models.