Exploring Microsoft SQL Server Analysis Services (SSAS) in Depth
Written on
Introduction to SQL Server Analysis Services
Hello everyone!
We are kicking off a new series focused on Microsoft SQL Server Analysis Services (SSAS). In this first article, I will provide a general overview, including what SSAS is and its key features, followed by a discussion on the differences between the Tabular and Multi-dimensional models offered by SSAS. I hope this content proves to be helpful—happy reading! :)
What is Microsoft SQL Server Analysis Services (SSAS)?
Initially known as Microsoft Analysis Services (MSAS) during the SQL Server 2000 era, it was rebranded to SQL Server Analysis Services (SSAS) with the release of SQL Server 2005. SSAS is an integral part of Microsoft SQL Server, which is a relational database management system (RDBMS).
SSAS serves as an ideal tool for business intelligence (BI) applications, providing robust data mining and Online Analytical Processing (OLAP) capabilities. It efficiently utilizes data gathered from various data sources and relational databases, delivering information swiftly from multiple origins.
In SSAS, cubes represent optimized multi-dimensional databases designed for data storage and OLAP applications. In essence, SSAS incorporates OLAP and data mining functions tailored for business intelligence applications.
Key Features of Microsoft SQL Server Analysis Services
Microsoft’s Analysis Services form the backbone of BI systems for numerous organizations. The advantages it offers can be summarized as follows:
- It eliminates the overhead of processing and storage, allowing for significant time savings.
- SSAS can handle terabyte-scale databases and thousands of users due to its high scalability.
- To support multiple users, SSAS services can horizontally scale, reducing resource conflicts and costs by increasing computing power and storage capacity.
- Quick response times mean that your BI applications can meet user requirements and keep pace with your business's demands.
Data collected from the RDBMS is transferred to SSAS cubes using analytical service projects, creating an analytical database that can be utilized for various purposes once ready.
SSAS services provide rapid access to large volumes of pre-aggregated data, enabling end users to obtain real-time insights into the business data they care about. A secure setup allows users to view the cubes they have defined. Additionally, it offers OLAP capabilities from diverse data sources, leveraging hierarchical levels based on dimensional relationships. This also facilitates multi-dimensional analysis through cubes, which allows for analyzing substantial datasets more efficiently than traditional two-dimensional relational views.
Types of SSAS Modeling
Before embarking on any BI project with SQL Server Analysis Services (SSAS), it's crucial to decide whether to utilize the Tabular or Multi-dimensional model. Making the right choice is vital because, once you select a model (either Multidimensional or Tabular), you cannot change it without restarting the process.
Tabular Model
- Organizes data into relevant tables.
- Easier to understand and create compared to the Multi-dimensional model.
- Performs faster for column-based queries than multi-dimensional cubes.
- Does not require hardware such as disks; however, it is memory-dependent, meaning more memory yields better performance.
- Since the entire database resides in memory, ample RAM is necessary. If your database size exceeds the server's memory capacity, transitioning to a Multi-dimensional model may be necessary.
- CPU speed is also a critical factor for Tabular models, and it is advisable to query using DAX (Data Analysis Expressions), which is the recommended query language for tabular models.
Multi-Dimensional Model
The Multi-dimensional model is composed of cubes. It provides a dimensional grouping that allows us to query data using dimensions and cubes. It works better with large datasets—especially terabytes—where a Multi-dimensional database is preferable. If your database demands significant storage (over 5 terabytes), adopting the Tabular model is not feasible; the only solution is the Multi-dimensional approach.
Additionally, while processing a table in a Tabular model does not affect other tables, in a Multi-dimensional model, processing one dimension can slow down processing for other dimensions.
In summary, SSAS can serve as a semantic layer for all your BI needs. This article provides a general introduction, and in subsequent articles, we will delve into step-by-step projects using Visual Studio for Tabular model setups and explore topics like Partition-Perspective in SSAS.
