Data modeling methods

Data modeling methods are techniques used to represent and organize data within a database or data storage system. These methods, such as Entity-Relationship (ER) modeling and dimensional modeling, help define the structure, relationships, and constraints of the data, enabling efficient querying and analysis.

Analytical data processes

Analytical data types

Centralized data management

Data architectures

Data mesh and data fabric

Data modeling methods

Data warehouse and data lake

Decentralized data management

Enterprise data management

Data vault modeling

The data vault modeling approach was invented by Dan Linstedt in 1990, and is used to model the enterprise data warehouse core layer. This approach is suitable for multi-source environments needing a fast adaptation to changes.

Dimensional modeling

Dimensional modeling, also known as the Star Schema approach, is a set of techniques and concepts used in data warehouse design proposed by Ralph Kimball.

Relational modeling

Relational data modeling is usually used for operational systems (OLTP systems) that store their data on relational databases, and also used for structured analytical data solutions.

Why biGENIUS-X?

Use cases

biGENIUS-X is the perfect solution for data-driven organizations that need to stay agile and efficient. It can be used for a wide range of industries and use cases.

Modern development processes

biGENIUS-X equips your team with all necessary tools to deal with complex data challenges to better analyze data.

Future-proof your data

With a high-level of automation, you can rest assured to be able to adapt to ever-changing technologies.

Build modern analytical data solutions

Modernization that scales and adapts to your business needs, no matter the size of your organization.

Implement decentralized approaches

Smart data automation allows your team to build data products more efficiently while implementing a data mesh.

Features

Produktübersicht

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