Learning Path for Dimensional Modelling

New to this topic? Not sure where to start? For your convenience, we have created the below learning path where articles are organised in the order of their relevance and complexity. Start from the beginning and read one by one to master the subject.

1Classifying data for successful modelingBasic 
2The 101 Guide to Dimensional Data ModelingBasic 
3Dimensional Modeling SchemaBasic 
4History Preserving in Dimensional ModelingIntermediate 
5Dimensional Modeling Approach for Various Slowly Changing DimensionsBasic 
6Implementing Rapidly changing dimensionIntermediate 
7Common Mistakes in Data ModellingBasic 
8Performance Considerations for Dimensional ModelingIntermediate 
9Top 50 Data Warehousing Interview Questions with AnswersBasic
10Top 50 DWBI Interview Questions with Answers - Part 2Basic
11Top 50 DWBI Interview Questions with Answers - Part 3Basic

Continuation to our collection of Data Warehouse Conceptual Questions. In case you need to refer the previous article click here

This article is the continuation of the article "Top 50 DWBI Interview Questions with Answers" from the Previous Page <<

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.

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 it’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.

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.

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.

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.

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.

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.

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.

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.

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