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Dimensional data modeling is a technique used in data warehousing to organize and structure data in a way that makes it easy to analyze and understand. In a dimensional data model, data is organized into dimensions and facts.
A dimensional model in data warehouse is designed to read, summarize, analyze numeric information like values, balances, counts, weights, etc. in a data warehouse. In contrast, relation models are optimized for addition, updating and deletion of data in a real-time Online Transaction System.
Dimensional modeling (DM) is part of the Business Dimensional Lifecycle methodology developed by Ralph Kimball which includes a set of methods, techniques and concepts for use in data warehouse design.
Drawn from The Data Warehouse Toolkit, Third Edition, the “official” Kimball dimensional modeling techniques are described on the following links and attached .pdf: Fundamental Concepts. Gather business requirements and data realities; Collaborative dimensional modeling workshops; Four step dimensional design process; Business processes; Grain
Dimensional data modeling is an analytical approach used in databases and data warehouses for organizing and categorizing facts into dimension tables. This type of modeling enables fast retrieval of information from large datasets by providing a structure that separates out unrelated or inconsequential data from the main body.
Kimball’s approach focused on modeling data in a way that aligns with business processes and user requirements, emphasizing simplicity and ease of use. In this article, we will delve deep into the concepts of dimensional data modeling and understand its processes, benefits and limitations. What is a Dimensional Data Model?
Dimensional data modeling is a data modeling technique that allows you to organize your data into distinct entities that can be mixed and matched in many ways. That can give your stakeholders a lot of flexibility.