Since the first beta of Analysis Services 2012, I worked with many companies designing and implementing solutions based on Analysis Services Tabular. I am glad that Microsoft published a white paper about a case-study using one of these scenarios: An Analysis Services Case Study: Using Tabular Models in a Large-scale Commercial Solution. Alberto Ferrari is the author of the white paper and many people contributed to it. The final result is a very technical document based on a case study, which provides a level of detail that I don’t see often in other case studies (which are usually more marketing-oriented). This white paper has the following structure: Requirements (data model, capacity planning, client tool) Options considered (SQL Server Columnstore Indexes, SSAS Multidimensional, SSAS Tabular) Data Model optimizations (memory compression, query performance, scalability) Partitioning and Processing strategy for near real-time latency Hardware selection (NUMA analysis, Azure VM tests) Scalability tests (estimation of maximum users per node) If you are in charge of evaluating Tabular as analytical engine, or if you have to design your solution based on Tabular, this white paper is a must read. But if you just want to increase your knowledge of Analysis Services, you will find a lot of useful technical information. That said, my favorite quote of the document is the following one, funny but true: […] After several trials, the clear winner was a video gaming machine that one guy on the team used at home. That computer outperformed any available server, running twice as fast as the server-class machines we had in house. At that point, it was clear that the criteria for choosing the server would have to be expanded a bit, simply because it would have been impossible to convince the boss to build a cluster of gaming machines and trust it to serve our customers. But, honestly, if a business has the flexibility to buy gaming machines (assuming the machines can handle capacity) – do this. Owen Graupman, inContact I want to write a longer discussion about how companies are adopting Tabular in scenarios where it is the hidden engine of a more complex solution (and not the classical “BI system”), because it is more frequent than you might expect (and has several advantages over many alternative approaches).
