10 SSAS Tabular Model Best Practices

When working with SSAS Tabular models, there are a few best practices to keep in mind in order to get the most out of your data. In this article, we'll go over 10 of the most important ones.

Microsoft SQL Server Analysis Services (SSAS) Tabular Model is a powerful tool for data analysis and reporting. It allows users to create data models that can be used to analyze data from multiple sources.

However, it is important to follow best practices when creating a SSAS Tabular Model. This will ensure that the model is optimized for performance and scalability. In this article, we will discuss 10 best practices for creating a SSAS Tabular Model. Following these best practices will help you create a model that is efficient and effective.

1. Use a single partition per table

Using a single partition per table allows for better query performance, as the engine can more easily identify which data needs to be retrieved. It also makes it easier to manage and maintain your model, since you don’t have to worry about managing multiple partitions. Finally, using a single partition helps ensure that all of your data is up-to-date, since any changes made to the underlying data will be reflected in the same partition.

2. Compress your data using VertiPaq compression

VertiPaq compression is a data storage and query optimization technique that reduces the size of your data by up to 10x. This means that you can store more data in less space, which leads to faster query performance and improved scalability.

VertiPaq compression works by analyzing the data stored in each column and then compressing it using an algorithm that takes into account the values stored in the column. The result is a compressed version of the original data that takes up much less space.

Using VertiPaq compression is one of the best ways to optimize your SSAS Tabular Model for better performance and scalability. It’s also easy to implement – all you need to do is enable the feature in the model properties.

3. Hide unused columns and tables

When a user queries the model, SSAS will scan all of the columns and tables in the model to determine which ones are needed for the query. If there are unused columns or tables, this can slow down the query performance significantly. By hiding these unused elements, you can improve query performance and make sure that users get the results they need quickly.

Hiding unused columns and tables also helps keep your model organized and easier to maintain. It’s much easier to find what you’re looking for when everything is neatly organized and labeled.

4. Avoid calculated columns in the model

Calculated columns are evaluated at the time of query, which can lead to performance issues. Additionally, calculated columns cannot be indexed and therefore will not benefit from any indexing strategies that you may have in place.

Instead, it is best practice to create measures for calculations within the model. Measures are evaluated at the time of processing, so they are much more efficient than calculated columns. Furthermore, measures can be indexed, allowing them to take advantage of any existing indexing strategies.

5. Avoid unnecessary relationships between tables

When you create a relationship between two tables, it creates an implicit filter on the related table. This means that any query against the related table will be filtered by the values in the primary table. If there are unnecessary relationships between tables, this can lead to slower performance and incorrect results.

To avoid this issue, make sure to only create relationships between tables when absolutely necessary. When creating relationships, also consider whether or not the relationship should be active or inactive. An inactive relationship does not apply any filters, so it is useful for cases where you want to join two tables but don’t need to filter one based on the other.

6. Use DAX measures instead of calculated columns

Calculated columns are stored in memory, which means that they take up a lot of space and can slow down the performance of your model.

DAX measures, on the other hand, are calculated dynamically when needed, so they don’t take up any extra space or cause performance issues. Additionally, DAX measures are more flexible than calculated columns because you can use them to create complex calculations that would be difficult to do with calculated columns.

7. Avoid many-to-many relationships

Many-to-many relationships can cause performance issues, as the query engine has to process more data than necessary. Additionally, many-to-many relationships can lead to incorrect results due to double counting of values.

To avoid these problems, it’s best practice to use a bridge table when modeling many-to-many relationships in SSAS Tabular Modeling. A bridge table is an intermediary table that contains foreign keys from two other tables and helps establish the relationship between them. This allows for better performance and accuracy when querying the model.

8. Use role playing dimensions

Role playing dimensions allow you to reuse the same dimension in multiple roles, such as a customer dimension that can be used for both sales and orders. This helps reduce the complexity of your model by eliminating redundant tables and columns.

Role playing dimensions also make it easier to create relationships between tables because you don’t have to worry about creating separate foreign keys for each role. Finally, they help improve query performance since the same dimension is being reused instead of having to join multiple tables together.

9. Use row level security to protect sensitive data

Row level security allows you to control which users can access specific rows of data in the model. This is especially important when dealing with sensitive information such as customer or financial data, as it ensures that only authorized personnel have access to this data.

Row level security also helps ensure that users are not able to view more data than they should be allowed to see. For example, if a user has permission to view sales data for their region, row level security will prevent them from viewing sales data for other regions.

Using row level security is an essential best practice for any SSAS Tabular Model and should always be implemented whenever possible.

10. Use Power BI Desktop for development

Power BI Desktop is a powerful tool that allows you to quickly create and modify SSAS Tabular Models. It also provides an easy-to-use graphical interface for creating relationships, measures, and calculated columns.

Power BI Desktop also has the ability to connect directly to data sources such as SQL Server, Oracle, and Azure Analysis Services. This makes it easier to develop models without having to manually write T-SQL or MDX queries. Additionally, Power BI Desktop can be used to test and debug your model before deploying it to production.


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