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What are the most effective ways to design a dimension table?

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Dimension tables are essential components of a data warehouse that store descriptive attributes of the entities in a business process. They help to provide context, meaning, and granularity to the facts that are measured and analyzed in a data warehouse. However, designing a dimension table is not a trivial task. It requires careful consideration of the business requirements, the data sources, the performance, and the usability of the data warehouse. In this article, we will discuss some of the most effective ways to design a dimension table, based on the best practices of dimensional modeling.

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Arockia Nirmal Amala Doss

Founder, Data Engineer @ ZippyTec GmbH | Data Migration & Data Engineering Consulting | Data Migration Coaching | AWS…

1 Choose the right level of detail

The first step in designing a dimension table is to determine the level of detail or granularity that the dimension table will represent. This depends on the business questions that the data warehouse will answer, and the availability and quality of the data sources. For example, if the business needs to analyze sales by product, customer, and date, then the dimension tables should have attributes that describe these entities at the lowest level of detail. However, if the data sources do not provide enough information or have inconsistencies or gaps, then the dimension table may need to aggregate or consolidate some attributes to a higher level of detail. The level of detail should balance the need for accuracy, completeness, and flexibility of the data warehouse.

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Akhil Vira

VP Equity Data Warehouse and Insights
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Granularity of dimensions cannot be lower than the base fact table whose dimensions we are defining. Ideally a dimension should have attributes or properties that do not change frequently. Hence new values identification and load processes of same should be structured and not heavy load. Some properties of dimensions that change frequently, e g. Weather of a City or Age of an employee should not be part of dimension table.

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2 Use surrogate keys

The second step in designing a dimension table is to use surrogate keys as the primary keys of the dimension table. Surrogate keys are artificial, sequential, and unique identifiers that are generated by the data warehouse, and are independent of the natural keys or business keys that are used by the data sources. Surrogate keys have several advantages over natural keys. They simplify the loading and updating of the dimension table, as they do not change over time or have duplicates or missing values. They also improve the performance and scalability of the data warehouse, as they are usually integers that require less space and are faster to join. They also enable the tracking of changes in the dimension attributes over time, by using techniques such as slowly changing dimensions.

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Akhil Vira

VP Equity Data Warehouse and Insights
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Surrogate key makes main fact table column size smaller and better for join performance. For type two dimensions surrogate key is very valuable as we can keep both old and new value applicable depending on date for which data is pulled.

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3 Normalize or denormalize

The third step in designing a dimension table is to decide whether to normalize or denormalize the dimension table. Normalization is the process of splitting a table into multiple smaller tables that have fewer columns and less redundancy, and are linked by foreign keys. Denormalization is the opposite process of combining multiple tables into one larger table that has more columns and more redundancy, and does not require joins. Normalization and denormalization have trade-offs in terms of complexity, performance, and storage. Normalization reduces the storage space and the risk of data inconsistency, but increases the complexity and the number of joins required to query the data. Denormalization simplifies the query and improves the performance, but consumes more storage space and may introduce data redundancy and inconsistency. The choice between normalization and denormalization depends on the size, structure, and frequency of changes in the dimension data, as well as the query patterns and expectations of the users.

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Arockia Nirmal Amala Doss

Founder, Data Engineer @ ZippyTec GmbH | Data Migration & Data Engineering Consulting | Data Migration Coaching | AWS Community Builder
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The normalization vs. denormalization debate is a classic one. I often say it's like choosing between a Swiss Army knife and a specialized tool. While normalization is elegant and efficient, denormalization is pragmatic for query performance. Over the years, I've learned it's not about a one-size-fits-all approach but understanding the specific needs of the business. Always ask: 'What will serve the end-user best?' Sometimes, it's a hybrid approach. Remember, it's about striking the right balance for your unique scenario.

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4 Add descriptive and meaningful attributes

The fourth step in designing a dimension table is to add descriptive and meaningful attributes that capture the characteristics and behaviors of the entities in the dimension table. These attributes should be relevant, consistent, and understandable for the users who will analyze the data. They should also be aligned with the business terminology and the data quality standards. Some examples of descriptive and meaningful attributes are names, labels, codes, descriptions, categories, hierarchies, flags, indicators, and measures. These attributes should be populated from the data sources or derived from business rules or calculations. They should also be validated and standardized to ensure accuracy and completeness.

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5 Optimize for performance and usability

Optimizing a dimension table for performance and usability is the fifth step in designing a dimension table. This involves applying techniques and best practices that can enhance the speed, efficiency, and quality of the data warehouse. Indexing, partitioning, compression, security, and documentation are some of these techniques. Indexing creates indexes on surrogate keys and frequently used attributes to speed up query and join operations. Partitioning divides the dimension table into smaller chunks based on criteria such as date, region, or category to improve loading and querying performance. Compression reduces the size of the dimension table by applying compression algorithms such as bitmap encoding, run-length encoding, or dictionary encoding to save storage space and improve query performance. Security measures such as encryption, authentication, authorization, or auditing should be applied to protect the dimension table from unauthorized access or modification. Lastly, documentation should include metadata such as definitions, sources, rules, formats, and changes to provide information and guidance to users and developers of the data warehouse.

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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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What are the most effective ways to design a dimension table?

1

2

3

4

5

6

1 Choose the right level of detail

2 Use surrogate keys

3 Normalize or denormalize

4 Add descriptive and meaningful attributes

5 Optimize for performance and usability

6 Here’s what else to consider

Data Warehousing

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What are the most effective ways to design a dimension table?

1

2

3

4

5

6

1 Choose the right level of detail

2 Use surrogate keys

3 Normalize or denormalize

4 Add descriptive and meaningful attributes

5 Optimize for performance and usability

6 Here’s what else to consider

Data Warehousing

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