Data Modeling

Data modeling for data warehouses and data marts is always a challenge for developers and architects. The modeling technique predefines the performance of the systems and their successful usage. Traditional as well as modern designs are used in the data warehouse environment. In general, the data in the core data warehouse is designed neutral (third normal form), while the data in data marts is designed application oriented.

This chapter provides deep insights in concepts and techniques of data modeling. I will present semantic modeling methods, data model schemas and particularities like “slowly changing dimensions” and “conformed dimensions”.


Part 01: Data Modeling Basics

A data model is a conceptual representation of data structures (tables) required for a database and is very powerful in expressing and communicating the business requirements. Data Modeling Basics A data model visually represents the nature of data, business rules governing the data and how it will be organized in the database. A data model […]

Part 02: Multidimensional data structures for OLAP

The article about traditional multidimensional databases is a summary of different blogs and explanation of myself. Definions MOLAP is a “multidimensional online analytical processing”. ‘MOLAP’ is the ‘classic’ form of OLAP and is sometimes referred to as just OLAP. But it is wrong to call it OLAP. MOLAP stores data in an optimized multi-dimensional array […]

Part 03: Modeling methodologies

“Data modeling in software engineering is the process of creating a data model for an information system by applying formal data modeling techniques.” Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, the process of data […]

Part 04: Kimball vs Imnon

These chapter is a blog entry by Dr. Mark Whitehorn specializes in the areas of data analysis, data modeling and business intelligence (BI). Based in the UK, he works as a consultant for a number of national and international companies and is a mentor with Solid Quality Mentors. In addition, he is a well-recognized commentator on […]

Part 05: Modeling Schemas

A data warehouse is an organized collection of large amounts of structured data. It is a database designed and intended to support decision making in organizations. It is usually batch updated and structured for rapid online queries and managerial summaries of its contents. According to Bill Inmon (1993), who is often called the “father” of […]

Part 06: Conceptual schema

A conceptual schema is a high-level description of a business’s informational needs. It typically includes only the main concepts and the main relationships among them. Typically this is a first-cut model, with insufficient detail to build an actual database.This level describes the structure of the whole database for a group of users. The conceptual model […]

Part 07: Entity–relationship model

An entity–relationship model (ER model) describes inter-related things of interest in a specific domain of knowledge. An ER model is composed of entity types (which classify the things of interest) and specifies relationships that can exist between instances of those entity types. Entity–relationship modeling was developed for database design by Peter Chen and published in […]

Part 08: Normalization vs. De-Normalization

There are two ways of designing a physical data base model. The first way is to normalize the data model in order to minimize the data redudancy. The second way is to denormalize the data model in order to empower the data analysis. Database normalization Database normalization or simply normalisation, is the process of organizing […]

Part 09: Star Schema

In computing, the star schema is the simplest style of data mart schema and is the approach most widely used to develop dimensional data marts. The star schema consists of one or more fact tables referencing any number of dimension tables. The star schema is an important special case of the snowflake schema, and is […]

Part 10: Snowflake Schema

In computing, a snowflake schema is a logical arrangement of tables in a multidimensional database such that the entity relationship diagram resembles a snowflake shape. The snowflake schema is represented by a centralized fact table which is connected to multiple dimensions. “Snowflaking” is a method of normalising the dimension tables in a star schema. When […]

Part 11: Galaxy Schema

A Galaxy Schema is like a Star or Snowflake schema a data model which contains fact tables and dimension tables. Compared to Star and Snowflake schema, a Galaxy Schema has always at least two fact tables and dimension tables. The fact tables are connected to the same dimension tables and are so called “conformed dimensions”. […]

Part 12: Particularity – Hierarchies

A hierarchy is an arrangement of items (objects, names, values, categories, etc.) in which the items are represented as being “above,” “below,” or “at the same level as” one another. In business environments, we do handle hierarchies every day. For example, Apple sells products in the category “mac”, “ipad”, “iphone”, “watch”, “tv”, “music”, “ipod” and […]