DAMA DMBOK: Data Modeling Tools

Hello everyone,

Welcome to my fifth article about data modeling. In this article I will try to explain data modeling tools. Have fun!

In the realm of data modeling, a wide array of tools exists to aid data modelers in their tasks, offering invaluable assistance throughout the entire process.

Here is some of the data modeling tools:

Entry Level Data Modeling Tools

Entry-level data modeling tools provide basic drawing functionality including a data modeling pallet so that the user can easily create entities and relationships. More sophisticated data modeling tools support forward engineering from conceptual to logical to physical to database structures, allowing the generation of database data definition language (DDL).

Lineage Tools

In data management, lineage refers to the history and relationships of data as it is transformed and moved through different stages or systems. Lineage tools are tools that are used to track and document the lineage of data in order to understand how it has been transformed and where it came from. Microsoft Excel® is a frequently-used lineage tool.

Data Profiling Tools

A data profiling tool can help explore the data content, validate it against existing Metadata, and identify Data Quality gaps/deficiencies.

Metadata Repositories

In data management, metadata such as the name, definition, and properties of data elements, as well as information about how the data is structured and used.

Data Model Patterns

There are three types of data model patterns are identified within DAMA DMBOK; elementary, assembly, and integration.

· Elementary data model patterns: Elementary data model patterns are the most basic and fundamental data model patterns. They represent the building blocks of more complex data models and include patterns such as the entity-relationship (ER) model and the object-oriented model.

· Assembly data model patterns: Assembly data model patterns are used to combine elementary data model patterns in order to create more complex data models. These patterns include patterns such as the star schema and the snowflake schema, which are commonly used in data warehousing.

· Integration data model patterns: Integration data model patterns are used to integrate data from multiple sources into a single data model. These patterns include patterns such as the enterprise data model, which is used to integrate data from multiple systems and organizations into a single, unified model.

Industry Data Models

Industry data models are data models pre-built for an entire industry, such as healthcare, telecom, insurance, banking, or manufacturing. These models are often both broad in scope and very detailed. Some industry data models contain thousands of entities and attributes.

In next article I’ll be ending this article series with ending notes and final words see you soon :)