Data Architecture

Data debt occurs when data is improperly handled at the technical level with the intention of postponing certain costs, even though the postponed costs will be higher, or the postponed benefits will be lower. The remainder of this document describes some important types of data debt.

In software-development & data architecture nirvana, the business analysts, database technologists, and application developers all speak the same language. Everyone agrees about what each user story means.

Data marts, data warehouses, and some operational datastores use dimension tables. A dimension table categorizes a fact table that joins to the dimension. At query time one filters the facts by values in the dimension table, and uses those values to label the query results

This post uses those concepts to survey the main types of relational architectures. These divide fundamentally into two types, the second having four sub-types: OLTP & BI.

This blog aims to become a “nutshell” treatment of the subject, so those of you who work with data in a relational database management system (RDBMS) can quickly learn how to make the best possible use of a database.