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Data Modeling

# Dimensional Model

Dimensional modeling is the name of a set of techniques and concepts used in data warehouse design to support end-user queries in a data warehouse. It is oriented around understandability and performance. Dimensional modeling always uses the concepts of facts (measures), and dimensions (context). Facts are typically (but not always) numeric values that can be aggregated, and dimensions are groups of hierarchies and descriptors that define the facts.

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Articles

## The 101 Guide to Dimensional Data Modeling

In this multi part tutorial we will learn the basics of dimensional modeling and we will see how to use this modeling technique in real life scenario. At the end of this tutorial you will become a confident dimensional data modeler.

Updated 28 Sep, 2020

## Classifying data for successful modeling

This paper discusses the natural characteristics of data in general. Understanding these characteristics help you classify the data appropriately while doing data modeling or data mining. This tutorial is written for the data modelers and data miners.

Updated 29 Sep, 2020

## Dimensional Modeling Schema

Now that we know the basic approach to do dimensional modeling from our earlier article, let us spend some time to understand various possible schema in dimensional modeling.

Updated 29 Sep, 2020

## History Preserving in Dimensional Modeling

In our earlier article we have seen how to design a simple dimensional data model for a point-of-sale system (as an example we took the case of McDonald's fast-food shop). In this article we will begin with the same model and we will see how we may enhance the model to store historical changes in the attributes of dimension table.

Updated 29 Sep, 2020

## Dimensional Modeling Approach for Various Slowly Changing Dimensions

In our earlier article we have discussed the need of storing historical information in dimensional tables. We have also learnt about various types of changing dimensions. In this article we will pick "slowly changing dimension" only and learn in detail about various types of slowly changing dimensions and how to design them.

Updated 29 Sep, 2020

## Implementing Rapidly changing dimension

Handling rapidly changing dimensions are tricky due to various performance implications. This article attempts to provide some methodologies on handling rapidly changing dimensions in a data warehouse.

Updated 29 Sep, 2020

## Common Mistakes in Data Modelling

A model is an abstraction of some aspect of a problem. A data model is a model that describes how data is represented and accessed, usually for a database. The construction of a data model is one of the most difficult tasks of software engineering and is often pivotal to the success or failure of a project.

Updated 29 Sep, 2020

## Performance Considerations for Dimensional Modeling

Performance of a data warehouse is as important as the correctness of data in the data warehouse because unacceptable performance may render the data warehouse as useless. There is this increasing awareness about the fact that itâ€™s much effective to build the performance from the beginning rather than to tune the performance at the end. In this article we have a few points that you may consider for optimally building the data model of a data warehouse. We will only consider performance considerations for dimensional modeling.

Updated 29 Sep, 2020

## Top 50 Data Warehousing/Analytics Interview Questions and Answers

This article attempts to explain the rudimentary concepts of data warehousing in the form of typical data warehousing interview questions along with their standard answers. After reading this article, you should gain good amount of knowledge on various concepts of data warehousing.

Updated 28 Aug, 2022

## Top 50 DWBI Interview Questions with Answers - Part 2

Updated 28 Aug, 2022