DAX Engines – The Formula Engine and The Storage Engine

Are you curious about the diverse career paths you can pursue after earning an MBA in Finance? Wondering which job profiles align best with your skills and aspirations? Let’s explore the exciting opportunities that await you in the world of finance. In this blog, we are going to discuss DAX engines. But first, let us gain some knowledge about the DAX. Data Analysis Expressions is a formula expression language used in Power Pivot in Excel, Power BI, and Analysis Services. It performs advanced calculations and queries on data by including functions, operators, and values.

DAX Engines

Two internal engines power DAX. The formula engine and the storage engine. By collaborating, these two engines evaluate DAX queries and combine and compress raw data.

It is critical to understand the distinction between these two engines. Each engine is designed for different purposes. Appropriately using the engine and your knowledge will provide you with faster and more accurate results. Let’s study and compare these engines based on their different qualities.

The following table outlines the differences between the formula engine and the storage engine on various aspects.

DAX Engines Comparisons: Formula Engine vs Storage Engine

Understanding from the above table

1. The storage engine handles simple requests. As a result, it is faster than the formula engine. Also, a storage engine is capable of doing many tasks by itself. For example, solving simple queries that require SUM, MIN, or MAX.

2. The formula engine can handle very complex functions. The number of formula engines in your expression depends on the complexity of your query. You will have a large number of formula engines in your expression if you are dealing with a very complex query.

3. The storage engine uses a compressed data cache. On the other hand, the formula engine uses an uncompressed data cache from the storage engine. Compressed data is easy to manage, whereas uncompressed data takes a lot of time and memory.

4. The storage engine is faster than the formula engine. The reason behind this is that the storage engine uses more than one core at a time. The storage engine, in other words, operates with multiple threads. It is capable of scanning the table segments based on the number of cores one has. Moreover, the formula engine can only do one core at a time.

5. Your measure produces the answer using the storage engine and stores it in the memory. This is not the case with the formula engine. Your measure produces the answer using the formula engine but does not store it in the memory.

6. The formula engine processes the request for a query plan and then generates and executes one. On the other hand, the storage engine answers the request made by the formula engine by retrieving data from the tabular model.

7. The storage engine periodically refreshes the copy of data present in the memory from the data source. On the other hand, the formula engine directly forwards every requested query to the original data source.

This was all about the differences between the formula engine and the storage engine. Let us now discuss their tasks individually.

Tasks of the formula engine

The formula engine is known as DirectQuery. It converts the DAX query into a query plan. For this, it has to execute many physical steps. The Formula engine executes specific operations in such a way that they all have a corresponding step in the query plan.

Filtering with complex conditions joins between the tables, aggregation, and lookups are some typical operators of the formula engine. These operators sometimes require data from the columns in the data model. In situations like these, the formula engine sends a request to the storage engine. The storage engine further provides an answer by returning a data cache.

Additional information

The storage engine creates a temporary storage area known as the data cache. They contain the result of the storage engine query. Data caches do not compress. They are plain in-memory tables. Regardless of the storage engine they come from, data caches store data in an uncompressed format.

The above table mentions that the formula engine operates in a single-threaded manner. Hence, regardless of the number of cores available, any operation in the formula engine only uses a single thread or a single core. Also, the formula engine only sends a single query at a time to the storage engine. Each request made to the storage engine has a certain degree of parallelism available. It can take advantage of multiple cores at a time as it has a different architecture.

Tasks of the storage engine

The storage engine is known as VertiPaq. It is also referred to as xVelocity in the Power Pivot. Most users commonly use VertiPaq as their storage engine.

There are two main goals of the storage engine. First, the database undergoes a scan. Second, it must produce data caches required by the formula engine.

Surprisingly, the storage engine is independent of DAX. The storage engine allows its own set of operators to execute queries exclusively. The set of operators in the storage engine might range from very limited to very rich. However, it completely depends on the kind of storage engine used.

Every table in a model can have a different storage engine technology depending upon the mode of datasets.

• Import: The VertiPaq engine stores the content of the table.

• DirectQuery: The content of the table is fetched from the data source at query time, without storing it in memory during data refresh.

• Dual: The VertiPaq engine loads the table into memory during data refresh. Additionally, the table can be queried in DirectQuery mode to access the most current information available.

The storage engine might also feature a parallel implementation. As discussed in one of the above paragraphs, the storage engine receives a request from the formula engine. It receives sequential requests and sends them synchronously.

As a result, the formula engine has to wait for the storage engine to finish the query before sending another one. Thus, the parallelism in the storage engine might tend to reduce due to a lack of parallelism in the formula engine.

You can also check a Comprehensive Guide on DAX using Power BI.  We have written a lot in detail for you. Give your input as well. 

Conclusion

This was all about DAX Engines – Storage engine and Formula engine. We don’t think that we need to answer which one is better. It’s quite clear from the above information. What are your thoughts? Let us know in the comment section.

Want to know about the 21 Ways To Optimize DAX Queries & Power BI Report?

FAQ – DAX Engines

1. How can understanding DAX Engines help our analytics team build faster Power BI dashboards?

If you understand how the Formula and Storage Engines work, you can design reports that process data more efficiently. The Formula Engine handles complex calculations but is slower, while the Storage Engine focuses on quick data retrieval. Balancing both helps analysts create dashboards that load faster and handle larger datasets without lag.

2. What are the typical bottlenecks caused by the Formula Engine in enterprise Power BI models, and how can training solve them?

Bottlenecks usually occur when multiple nested DAX measures or complex logic overload the Formula Engine. Through corporate Power BI training, teams can learn to restructure calculations, simplify dependencies, and optimize DAX expressions, reducing CPU load and improving query response times.

3. Should our BI team focus more on Storage Engine techniques when we deal with large datasets and many users?

Yes, because the Storage Engine is optimized for speed and parallel processing. It can handle millions of records efficiently when models are well-structured. Teams dealing with high data volume or shared reporting environments should focus on storage optimization, compression techniques, and caching strategies to improve performance.

4. How does knowledge of DAX Engines tie into performance optimization training for Power BI in a corporate setting?

In enterprise analytics, knowing how DAX Engines process queries is the foundation of performance tuning. It helps teams locate where time is spent, data retrieval or computation, and optimize accordingly. Corporate training programs often focus on query diagnostics, resource allocation, and real-time performance monitoring to ensure reports stay responsive.

5. Can we get professional training that includes practical exposure to DAX Engines and Power BI performance tuning?

Yes. Platforms like Edu4Sure offer corporate training programs that include dedicated modules on DAX Engines. These sessions combine conceptual learning with real Power BI case studies, helping participants understand how both engines work and how to optimize reports for business use. Teams walk away with hands-on strategies to make dashboards faster, lighter, and more reliable.

Looking for DAX Training for you or your employees? Kindly connect at partner@edu4sure.com or call us at +91-9555115533.