Prior to SQL Server 2016, Excel couldn't be used since Excel passes MDX rather than DAX. Excel is now available as a tool to use with DirectQuery mode.The computation will be pushed down to the database server.
Calculated columns in the Tabular model are now permitted when in DirectQuery mode. Support for Row-Level Security in the underlying data source, which is a new feature in SQL Server 2016. Now the options include including SQL Server, APS (PDW), Oracle, and Teradata. Additional relational data sources are now supported. Performance improvements related the queries that are generated. Improvements to DirectQuery in SQL Server 2016: Updates to SSAS Tabular in DirectQuery Mode in SQL Server 2016 Only the most straightforward DAX calculations will be able to convert to SQL. If you tend to pass most everything you need into SSAS from the source database, and your SSAS calculations are simple, you should be able to consider DirectQuery mode. Most or All Logic Comes from Underlying Database. In that case, the value of SSAS is the semantic layer (i.e., if you have users connecting to the data source and developing their own reports, it is significantly easier for users to navigate an SSAS model where everything is organized and no joins are required). If you've invested time creating clustered columnstore indexes, partitions, or other query tuning efforts in the underlying database engine, you may be inclined to utilize the source database for live query activity. Relational Database Highly Tuned for Ad Hoc Queries. In the absence of model processing, SSAS would have much less CPU and memory demands when running in DirectQuery mode (and conversely, the underlying DB engine needs to be beefier). Therefore, for large datasets that are difficult to fit in memory, DirectQuery can be appealing. The rule of thumb for storing data in a Tabular in-memory model is to have 2.5x memory, so if your in-memory model size is 50GB, you would require about 125GB of RAM on the server. Another situation to consider DirectQuery is if your server doesn't have enough memory to store all of the data. Large Datasets Which Exceed Memory Available. #Ssas tabular cube how to#
In this situation, you'll want to plan for how to handle contention of read and write activity. Because there's no processing time associated with populating data in SSAS, there's less delay in making data available.
Low latency for data availability is the primary use case for DirectQuery. However, there are some specific situations where DirectQuery is a viable option. Most SSAS Tabular models do run in In-Memory mode. Use Cases for SSAS Tabular in DirectQuery Mode Note that the entire SSAS Tabular model is specified as one of the two modes.
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