How do you use conformed dimensions to integrate data from multiple sources?

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If you want to design a data warehouse that can store and analyze data from multiple sources, you need to understand the concepts of dimensional modeling and star schema. These are techniques that help you organize your data in a way that makes it easy to query, report, and perform business intelligence tasks. In this article, you will learn what dimensional modeling and star schema are, how they differ from other data modeling approaches, and how you can use conformed dimensions to integrate data from different sources.

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Nebojsha Antic ??

?? Business Intelligence Developer | ?? Certified Google Professional Cloud Architect and Data Engineer | Microsoft ??…
Sagar Navroop

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Lidiane Nepomuceno Pecis

Microsoft Power BI Certified | Oracle BI Certified | ágil | Business Intelligence | Data Warehouse

1 Dimensional modeling

Dimensional modeling is a data modeling technique that focuses on the business processes and the data requirements of the end users. It involves identifying the main subjects or entities of your business, such as customers, products, sales, etc., and the measures or facts that describe them, such as revenue, quantity, cost, etc. These subjects and measures are then organized into two types of tables: dimension tables and fact tables. Dimension tables store the descriptive attributes of the subjects, such as customer name, product category, sales date, etc. Fact tables store the numerical measures of the subjects, such as sales amount, order count, profit margin, etc. Dimension tables and fact tables are linked by foreign keys, creating a star-like structure.

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Sagar Navroop

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Conformed dimensions play a pivotal role in integrating data from diverse sources within a data warehouse. In dimensional modeling, conformed dimensions are shared across multiple-star schemas, ensuring consistency and coherence in data analysis. To create them, identify common dimensions such as time, geography, or product, and harmonize their structures, attributes, and hierarchies across all relevant data sources. The benefits include improved data quality, streamlined reporting, and enhanced cross-functional analysis. However, challenges may arise in maintaining consistency across disparate systems and ensuring synchronization of updates. Conformed dimensions bring visibility into the holistic data integration landscape.

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Lidiane Nepomuceno Pecis

Microsoft Power BI Certified | Oracle BI Certified | ágil | Business Intelligence | Data Warehouse
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Um modelo dimensional deve traduzir com simplicidade um negócio. é importante utilizar nomenclaturas que fa?am sentido para os usuários que ir?o trabalhar com aqueles dados. N?o invente nomes apenas para deixar rebuscado e bonito! Procure utilizar em nomes de dimens?es, fatos e atributos uma linguagem que seja conhecida na organiza??o. Isso facilita a utiliza??o pelos usuários.

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2 Star schema

Star schema is the simplest and most common form of dimensional modeling. It consists of one fact table and one or more dimension tables, where each dimension table is directly connected to the fact table by a single foreign key. This structure makes it easy to join the tables and perform queries on the data. For example, if you want to know the total sales amount by product category and month, you can join the fact table with the product dimension table and the date dimension table, and then group by the product category and month attributes. Star schema also allows you to use simple and intuitive names for your tables and columns, making it easier for the end users to understand and work with the data.

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Lidiane Nepomuceno Pecis

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O esquema estrela é o mais simples de utilizar, criar e compreender dentro de um data warehouse. Mas, é importante entender se para o seu projeto ele é a melhor escolha. Ainda existem casos para os quais o modelo floco de neve (snowflake) pode ser mais interessante. Isso n?o depende apenas do negócio, mas principalmente das ferramentas de visualiza??o que ir?o consumir os dados, uma vez que algumas possuem melhor performance com o modelo snowflake. Nunca uttilize um ou outro modelo apenas por ser mais o famoso. Procure entender as reais necessidades do seu projeto para ter a melhor arquietura possível, pensando em performance, governan?a e dimens?es conformadas (conformed dimensions).

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3 Conformed dimensions

Conformed dimensions are dimension tables that have the same meaning and structure across different fact tables or data sources. They allow you to integrate data from multiple sources and create a consistent view of the business. For example, if you have two fact tables that store sales data from different systems or regions, you can use a conformed dimension table for the product entity, which has the same product attributes and values in both fact tables. This way, you can compare and aggregate the sales data by product across the different sources. Conformed dimensions can also help you avoid data redundancy and inconsistency, as you only need to maintain one copy of the dimension data.

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Punya Ira Anand

MS ITM at UT Dallas | Software & Data Engineering | 2x AWS Certified | Actively Seeking Full-Time Roles | Python, SQL, AWS, and Cloud Solutions
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Conformed dimensions play a key role in integrating data from multiple sources by ensuring consistency and alignment across fact tables. These dimensions have the same meaning and structure across data sources, enabling a unified view of the business. For example, if sales data is stored in two systems or regions, a conformed dimension for the product entity ensures the same attributes and values across both systems. This allows seamless comparisons and aggregations of sales data by product. Additionally, conformed dimensions reduce redundancy and inconsistency, as only one version of the dimension data is maintained, simplifying updates and enhancing data integrity.

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4 How to create conformed dimensions

To create conformed dimensions, you need to follow some steps and best practices. First, you need to identify the common dimensions that are shared by the different fact tables or data sources. These are usually the core business entities that are relevant for most of the analysis and reporting scenarios. Second, you need to define the attributes and levels of detail for each conformed dimension, and make sure they are consistent and aligned with the business requirements and terminology. Third, you need to populate and update the conformed dimension tables with the data from the different sources, and ensure the data quality and accuracy. Fourth, you need to link the conformed dimension tables with the fact tables or data sources by using foreign keys or surrogate keys.

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Nebojsha Antic ??

?? Business Intelligence Developer | ?? Certified Google Professional Cloud Architect and Data Engineer | Microsoft ?? AI Engineer, Fabric Analytics Engineer, Azure Administrator, Data Scientist
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- Start with a thorough analysis of the business processes and data entities to identify which dimensions are common across different data sources. This helps in understanding the scope and structure of the conformed dimensions. ???♂? - Standardize the attributes of conformed dimensions. This includes defining a uniform set of attributes, data formats, and naming conventions that apply across all data sources. ?? - Implement data quality checks as part of the process to populate and maintain conformed dimensions. This ensures that the data is accurate, consistent, and reliable, minimizing discrepancies and errors. ??

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5 Benefits and challenges of conformed dimensions

Conformed dimensions can provide many benefits for your data warehouse design and performance. They can enable cross-functional analysis and reporting, as you can combine and compare data from different sources and perspectives. They can also improve the usability and efficiency of your data warehouse, as you can reduce the number of tables and joins, and use simple and meaningful names for your dimensions. Moreover, they can enhance the data governance and integrity, as you can ensure the consistency and accuracy of your dimension data. However, conformed dimensions also come with some challenges and trade-offs. They can require more time and effort to design and implement, as you need to coordinate and harmonize the data from different sources and systems. They can also limit the flexibility and granularity of your data warehouse, as you need to adhere to a common definition and structure for your dimensions.

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How do you use conformed dimensions to integrate data from multiple sources?

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1 Dimensional modeling

2 Star schema

3 Conformed dimensions

4 How to create conformed dimensions

5 Benefits and challenges of conformed dimensions

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