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How does dimensional modeling simplify BI queries and reports?

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If you work with business intelligence (BI) tools, you know how important it is to have a reliable and efficient data warehouse. But how do you design and organize your data warehouse to support your BI needs? One popular approach is dimensional modeling, a technique that separates your data into facts and dimensions. In this article, you will learn what dimensional modeling is, how it benefits your BI queries and reports, and how to apply it to your data warehouse.

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1 What is dimensional modeling?

Dimensional modeling is a way of designing your data warehouse that focuses on the business questions you want to answer, rather than the operational details of your data sources. It involves creating two types of tables: fact tables and dimension tables. Fact tables store the quantitative measures of your business, such as sales, revenue, or costs. Dimension tables store the descriptive attributes of your business, such as product, customer, or time. By joining fact and dimension tables, you can create multidimensional views of your data that support various BI scenarios.

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2 How does dimensional modeling benefit your BI queries and reports?

Dimensional modeling has several advantages for your BI queries and reports. First, it simplifies your data structure by reducing the number of tables and columns you need to query. Second, it improves your query performance by minimizing the joins and aggregations you need to perform. Third, it enhances your data quality by ensuring consistency and accuracy across your data sources. Fourth, it increases your data usability by making your data more understandable and meaningful for your end users.

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Vineet D.

Senior Business Intelligence Engineer | Marquis Who’s Who Honored Listee
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For example, let’s say we are a car dealership like carvana. We can create dimension models which has various attributes of a car such as make, model, year of manufacture, color, number of cylinders, horsepower etc. now these are the attributes that define a car. Now we can have an orders table (fact table) that have the sales of the cars made by carvana. Both the tables can be joined on a key and we can find the number of sales made for a particular kind of car. Dimension table can also be reused to find how many cars we got the manufacturer and how many we sold. Dimension and fact tables can also help in creating shared datasets in Power BI, which can immensely reduce report production timeline.

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3 How do you apply dimensional modeling to your data warehouse?

To apply dimensional modeling to your data warehouse, you need to follow four steps. First, you need to identify the business processes and events that generate your data, such as orders, shipments, or payments. These will become your fact tables. Second, you need to identify the dimensions that describe your facts, such as product, customer, or time. These will become your dimension tables. Third, you need to define the relationships between your facts and dimensions, such as one-to-many, many-to-many, or degenerate. These will determine how you join your tables. Fourth, you need to design the schema of your tables, such as star, snowflake, or galaxy. These will affect how you store and access your data.

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Paul Mascard

Always striving to add value to Business Intelligence, Data Warehouse, Data Management and Data Governance teams.
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When defining your facts and dimensions, be very conscious of the role of the source field. Often the tendency is to assume numerical values are measures and should be recorded in a fact table. This is not always the case. For example, Loan Amount Requested would outwardly appear to be a financial figure you would wish to measure as a part of your mortgage or loan reporting. However, this only fixes at the point the loan commences. Until that point the value may change, as the customer requests a loan for more or less. These changes can’t be aggregated as transactions and are, in fact, attributes of the application, that have changed, and can be considered application/quote dimension attributes.

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4 What are some best practices for dimensional modeling?

Dimensional modeling is a flexible and adaptable framework that can be tailored to different data scenarios. To achieve optimal results, there are some best practices to consider such as using descriptive and consistent names for tables and columns, surrogate keys for dimension tables, conformed dimensions for integration and comparison of fact tables, hierarchies and levels to organize attributes for drill-down and roll-up analysis, slowly changing dimensions to handle changes in dimension data over time, and factless fact tables to capture events or conditions without measures.

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5 What are some challenges and limitations of dimensional modeling?

Dimensional modeling is not a perfect solution for every data problem, and there are some challenges and limitations that must be addressed. These include needing a clear understanding of business requirements and goals, which may not always be available or stable. Additionally, dimensional modeling may not capture all nuances and complexities of data sources, nor meet all analytical needs of end users. Lastly, it may not scale well with large or complex data sets, which may require partitioning, indexing, or compression.

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How does dimensional modeling simplify BI queries and reports?

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1 What is dimensional modeling?

2 How does dimensional modeling benefit your BI queries and reports?

3 How do you apply dimensional modeling to your data warehouse?

4 What are some best practices for dimensional modeling?

5 What are some challenges and limitations of dimensional modeling?

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