Background in the Era of Big Data In the family of Microsoft SQL Server, SQL Server Analysis Services (SSAS) comes up as an ideal data mining and multi-dimensional online analytical processing (OLAP) tool, especially for BI applications. It facilitates users in designing, creating, and managing multi-dimensional structures/mining models with data collected from disparate data sources/relational databases and with the help of data mining algorithms. This offers an augmented level of decision-making for better business output. The longtime success of SSAS has resulted in the creation of a huge value in terms of the management of structured data (cubes). As companies relied more and more on SSAS, the explosive growth in data volumes stressed its single server architecture to the breaking point. Data Volume Some of the tables have many columns and millions of rows. When the model is being processed, all the memory of the server is consumed.The SSAS multi-dimensional mode is more suitable for processing small volumes of data, but performance suffers when querying or cubing large datasets, especially with many dimensions. Scalability Existing SSAS processing capabilities have run into the limitations of single server architecture. To improve performance, users have no choice but to scale up with larger, high-end servers with a large number of cores, more RAM, and faster, larger disks. Concurrency Limitation of a single server architecture is the ability to support large numbers of concurrent users. SSAS may struggle or fail when too many users query at the same time. Once again, a distributed architecture could provide improved concurrency.