How do you use conformed dimensions to ensure consistency across different subject areas?

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If you are working with dimensional modeling, you know how important it is to design your data warehouse in a way that supports consistent and accurate analysis across different subject areas. One of the key concepts that can help you achieve this is conformed dimensions. In this article, you will learn what conformed dimensions are, why they are useful, and how you can use them to ensure consistency across different subject areas.

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Ammar Sayed ????????

Data Engineer @ Rackspace Technology | Databricks Certified | Lifelong Learner
Van Yancey

Analytics Professional
Naga venkata siva Gadde

Big Data Engineer

1 What are conformed dimensions?

Conformed dimensions are dimensions that have the same meaning and values across different fact tables or subject areas. For example, a conformed dimension of time can have the same attributes and levels of granularity for sales, inventory, and customer service facts. Conformed dimensions allow you to join and compare data from different sources without having to worry about data quality or compatibility issues.

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Ammar Sayed ????????

Data Engineer @ Rackspace Technology | Databricks Certified | Lifelong Learner
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For example, let's say you have two departments (inventory, and sales) - each fact table has its attributes and its dimension tables too for example: -- Sales Fact Tables (have information about the sales of these products, customers, and territories) -- Inventory Fact Tables (have information about the orders, warehouse, and vendors of these products) - both departments will have dim tables for the product and date info for these products. - so, rather than creating the same dim tables in the two departments twice, we can build Conformed Dimension Tables that share the same information with both departments. - Here the conformed dimensions can have the advantage of saving the data duplication.

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Naga venkata siva Gadde

Big Data Engineer
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A confirmed dimension is a common way to organize information that can be used in different areas of a business. At least this is attached to two fact tables.

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2 Why are conformed dimensions useful?

Conformed dimensions are useful because they enable you to perform cross-functional analysis and reporting on your data warehouse. For example, you can use a conformed dimension of customer to analyze the relationship between customer satisfaction, loyalty, and profitability across different channels and products. Conformed dimensions also simplify the maintenance and governance of your data warehouse, as you only need to update and validate one version of the dimension instead of multiple copies.

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Lindokuhle Magudulela

Thinker. Engineer | Data Specialist | Africa Business Strategy | Yellow Equipment
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One thing I have found useful is that they also ensure that you have a single point of change for your dimension. Using the sales dept and inventory example, where the sales fact table and inventory/orders tables are related to the product dim table(conformed). The developer only has to maintain one product dim table. This guarantees data integrity. Futhuremore this also simplifies the queries one would have to develop when connecting to the BI and analytics tools. For example views from the fact tables and the comformal dim tables can easily be linked in PowerBi and interactive reports with filtering that works on across fact tables can be created.

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3 How do you create conformed dimensions?

To create conformed dimensions, you need to follow some best practices and steps. First, you need to identify the common dimensions that are shared by different subject areas or business processes. Second, you need to define the attributes and hierarchies of these dimensions, and ensure that they are consistent and meaningful across all sources. Third, you need to implement the conformed dimensions in your data warehouse, either by using a single physical table or by using views or aliases that point to the same source.

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Kritika Mitra

Lead at HTC Global Services with expertise in Power Platform | Data Modeling & Data Warehouse | X5 Microsoft Certifications
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Creating conformed dimensions involves ensuring consistent and shared dimensions across different data marts within a data warehouse. Start by identifying common dimensions (e.g., time, customer, product) across business processes. Define standardized attributes, data types, and hierarchies for these dimensions. Design centralized dimension tables using surrogate keys for unique identification. Develop ETL processes to extract, transform, and load data into these dimension tables. Implement version control using Slowly Changing Dimensions (SCD) techniques to manage changes over time. Store conformed dimensions in a central repository and ensure regular updates across all data marts. Perform consistency checks and validate with stakeholders.

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4 How do you use conformed dimensions in queries and reports?

To use conformed dimensions in queries and reports, you need to join the fact tables that share the same conformed dimension on the corresponding dimension keys. For example, if you want to compare the sales and inventory performance by product and region, you can join the sales fact table and the inventory fact table on the product key and the region key, which are both conformed dimensions. You can then use the attributes and hierarchies of these dimensions to slice and dice your data and create reports and dashboards.

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5 What are some challenges and pitfalls of conformed dimensions?

Conformed dimensions can present certain challenges and pitfalls, such as managing changes and updates to attributes or hierarchies, balancing granularity and performance, and handling exceptions and variations. Managing updates can be time-consuming and error-prone, especially for complex data warehouses. Performance issues can arise when joining large fact tables or generating detailed reports. To avoid conflicts or confusion, you may need to use techniques such as subtypes, bridges, or outriggers to accommodate exceptions or variations across different subject areas or sources.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Van Yancey

Analytics Professional
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Conformed dimensions can be static or unchangeable such as eye color or date of birth for a customer, or they can be slowly changing such as customer's home address. Storing and joining changing dimensions should be considered in the overall data modeling strategy.

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How do you use conformed dimensions to ensure consistency across different subject areas?

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1 What are conformed dimensions?

2 Why are conformed dimensions useful?

3 How do you create conformed dimensions?

4 How do you use conformed dimensions in queries and reports?

5 What are some challenges and pitfalls of conformed dimensions?

6 Here’s what else to consider

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