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Business analysts use a variety of tools to analyze data. There are many traditional OLAP engines, among which Microsoft SQL Server Analysis Services (SSAS) is one of the most widely used in the world. In the family of Microsoft SQL Server, SSAS comes up as an ideal data mining and multidimensional online analytical processing (OLAP) tool, especially for BI applications. It facilitates users in designing, creating, and managing multidimensional 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.
Many large enterprises are deep and long-time users of SSAS. SSAS has many excellent features that have made it an ideal choice for many traditional business intelligence solutions. But it has faced increasing challenges in the era of big data. With the explosive growth of data volumes, these challenges have become particularly acute.
There are many traditional OLAP engines on the market. Oracle, SAP, IBM, and Microsoft all have their own OLAP engines. The role of an OLAP engine is to provide high-performance analytics and an enterprise-level semantic data model and use it for business intelligence reports and client applications. Their most important business value is to turn raw data in the database into a business-centric and user-friendly semantic model, and finally, to query the data. On Azure, you can also access its sibling Azure Analysis Services (AAS). However, it is important to note that AAS and SSAS are NOT the same thing. In particular, AAS does not offer multi-dimensional modeling. The success of SSAS is linked to its following characteristics:
The longtime success of SSAS has resulted in the creation of a huge amount of value in terms of the creation and organization of structured data (cubes). But SSAS has experienced a bit of its own success crisis. As companies relied more and more on SSAS, the explosive growth in data volumes stressed its single server architecture to the breaking point. At the same time, BI and analytics teams have raised expectations for processing huge datasets with the new era of cloud platforms and big data tools. While this has seemingly left SSAS behind, Kyligence provides a powerful path forward for the massive amount of valuable intellectual property that lives in SSAS data and applications.
The SSAS multidimensional mode has acceptable performance with small volumes of data, but performance suffers when querying or cubing large datasets, especially with many dimensions. Like most of the other MOLAP solutions, SSAS pre-calculates every intersection of the dimension combination by all measures, this results in the curse of dimensionality. The cube can become so large that it cannot be calculated within a reasonable time frame.
Existing SSAS processing capabilities have run into the natural limitations of a single server architecture. To improve performance, you have no choice but to scale up with larger, high-end, multi-core servers with a large number of cores, more RAM, and faster, larger disks. But there is no escaping the architecture limits of a scale up strategy for SSAS. Unfortunately, there is no notion of a SSAS cluster for scale out.
Another 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. With the growth of the citizen data scientist phenomenon, the need to support more and more concurrent users will become a critical requirement of any future analytics architecture.
Today, with big data technology evolving, an ideal BI/OLAP analysis architecture should retain all SSAS analysis capabilities and leverage the scale-out capability of a big data approach. This could address all of the pain points that SSAS faces. Such architecture should enable the following:
Kyligence is now working with many companies to help them migrate from a traditional business intelligence infrastructure to a big data architecture. Some of these customers are also working closely with Kyligence to solve the additional challenge of migrating from their SSAS solution. We included two case studies to help illustrate the ways in which Kyligence can help resolve the disadvantages caused by a reliance on SSAS.
In this real-world example, a Fortune 500 restaurant chain wanted to migrate their restaurant operational analytics solution away from SSAS towards a cloud solution. The goal was to establish a unified big data analysis platform to improve efficiency and support future growth.
Challenges Caused by SSAS
Benefits of Migrating Away From SSAS
In this real-world example, A large-scale financial services company relied on one of the largest SQL Server Analysis (SSAS) OLAP cubes in the world to support their risk management analysis, but the current solution was running into scaling limits due to increased data volumes.
There have been many advantages of the SSAS platform that have made it a popular OLAP platform. But many companies are looking for ways to bring the technology forward to a more modern, big data infrastructure. Limited scalability and limited data capacity make it difficult for SSAS to meet the requirements of the big data era. Kyligence can help customers bring all of the value that they created with SSAS forward into the future.
Kyligence's big data solutions provide customers with the following value:
Kyligence not only offers many of the same excellent capabilities of SSAS, but it also overcomes the limitations of SSAS by providing customers with the following value:
Interested in learning more? Discover how Kyligence can Replace Microsoft SSAS and Scale OLAP on Modern Big Data Architecture.
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