All About SQL Server Analysis Services (SSAS) In An Easy Manner


September 1, 2020


June 13th, 2023

Of the plethora of tools and technologies that Microsoft offers, Analysis Services is one enriched area that empowers its users with enterprise-level competencies for business intelligence and analytics services, data visualization, and reporting tools. The three main platforms for analysis services are:

  • SQL Server Analysis Services
  • Power BI Premium
  • Azure Analysis Services

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 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.


What Is SSAS?

Installed as an on-premises or VM server instance, SQL Server Analysis Services supports tabular models at all compatibility levels (depending on the version), multidimensional models, data mining, and Power Pivot for SharePoint. – Microsoft

Earlier, during the SQL Server 2000 version, the analysis service was known as Microsoft Analysis Services (MSAS). Microsoft had bought it from Panorama Software in the year 1996. With SQL Server 2005, it is called SQL Server Analysis Services (SSAS).

SSAS Can Be Defined Through The Following Characteristics:
  • Creation of cubes with data marts/data warehouses for insightful and quicker data analysis
  • Two different modes – Native mode and Share Point mode
  • Two major alternatives – Multidimensional (for corporate analytics) and Tabular (for personal and team analytics)
  • SSAS Multidimensional is Corporate BI
  • A blend of server and client technologies, value-added with a specialized environment for developing and managing it
  • It is installed as a VM server instance or as an on-premise model
  • Endorses tabular models at all compatibility levels
  • Enables slicing and dicing of the huge bulk of data

Key Features Of SSAS

  • Row-level and dynamic security available with automatic backup available
  • Supports partitioning and size is limited to memory
  • Querying cubes through multidimensional expressions
  • Adaptable creation and management of data models
  • API level backward compatibility possible
  • Works perfectly with different wizards and designers
  • Faster response to queries owing to the aggregation of facts
  • Competency to create a robust data model for enhanced reporting
  • All-time accessibility, at any place via the Internet
  • Assists in creating MOLAP, ROLAP and HOLAP architecture
  • Empowers working on offline and client-server mode both

How Does SSAS Operate?

SSAS operates on two major components – Business Intelligence Studio and SQL Server Data Tools. The fundamental concept of operating an SSAS environment lies in building and managing a cube.

A cube in SSAS is a multi-dimensional database finetuned for data warehousing and OLAP applications. It leverages data in the finest form and offers information quickly from multiple data sources.

The data warehouse is a store for enterprise-wide information from a variety of data sources coming from different formats. The main aim of a cube is to enhance query time in any RDBMS. MDX is the query language utilized to manage tasks with OLAP cubes.

Creating the SSAS cube needs the data warehouse ready and then SSIS can be leveraged to perform the ETL process with popular tools. You must build a novel Analysis Services project and then define a data source, evoking the cube wizard.

Here is a depiction of association between various Analysis Services objects:


Why Should SSAS Be Used?

SSAS is popular owing to the multiple benefits that it yields, some of them are as listed below:

  • Considered apt for numeric analysis, business intelligence solutions
  • Assists in preventing a clash of resources
  • Evolution of data patterns that are not easily available from data mining
  • An integrated and detailed look at all business information, KPIs, etc., to map with business objectives
  • Leverage dimensional relationships based on hierarchical levels
  • Make in-depth business decisions
  • Use aggregated facts for extracting needed information, making it fast
  • Storage of SSAS results in a cache, for quick retrieval and faster output
  • Easy navigation into data by slicing, dicing, and drilling down of information
  • Effective handling of the increased number of concurrent users
  • Simple to manage advanced time calculations like a 1-year average etc.
  • A secure set up to let users view their own designated cubes
  • Inbuilt support for KPIs with an abstraction layer between the user and the data warehouse
  • Utilization of powerful tools like Power View and Performance Point

Limitations Of SSAS

  • Not possible to shift from one selection to another between Tabular and Multidimensional, unless you start over again
  • Not permitted to merge information between the two different types of cubes
  • Sometimes it is tough to manage if requirements get altered halfway through the execution

SSAS Best Practices To Maximize Benefits

  • Users must define efficient aggregations for huge data groups and make the most of partitions
  • Aggregations must be created in balance, not too many, to disturb the performance
  • Effective MDX must be written
  • The cube must be optimized, and group design measured
  • Useful usage of the query engine cache is a must
  • Use the scaling up feature or scaling out feature as appropriate
  • Tuning of memory/processor usage
  • Create natural hierarchies for better performance

SSAS Tabular vs Multidimensional Model

Parameters Tabular Model in SSAS Multidimensional Model in SSAS
Definition Organization of data into relevant tables signifies a tabular model, which has a slack structure, used with the data compression technique Organization of data through a data cube is a multidimensional data model, which operates with a strict structure along with a data compression technique
Operational Use Usage of tables leading to faster execution, as all tables offer information as compared to facts and dimensions

Easy to use and develop

It executes as a group of operations that help users fetch query results through cube/dimension coordinates

Mature and scalable

Memory Engine Possesses in-memory cache and reads directly from the cache Operates on file-based storage system through OLAP cubes and reads pre-aggregated data
Data Language DAX (Data Analysis Expression) by Power Pivot

Tabular can interpret reports from both – DAX and MDX

MDX (Multidimensional Expression)

It can interpret reports from MDX but not from DAX

Data Organization Data need not change the place from the source place It operates at its finest when data takes its place in a star schema
Model Style Known to follow a relational model Known to follow a dimensional model
Used When Data is lesser, simpler, and non-complicated Data is in bulk, complicated, and larger size
Technologies Used Usage of modern compression algorithms and multi-threaded query processing Usage of multidimensional structure in cells at coordinates required by the dimensions
Data Access Mode Tabular supports two data access modes – Cached and DirectQuery Multidimensional supports two data access modes – ROLAP and MOLAP
Reporting Capabilities Better performance at a granular level since it reads columnar data from RAM Better performance for predefined aggregations as data is accessed from RAM
Tabular Model Can Be Selected Because
  • It works faster for queries depending on columns
  • It is easy to understand
  • It is dependent on memory and hence ensures better performance
  • It offers an effective data compression of about 1/10th of the size as compared to 1/3rd of size in multidimensional mode
Multidimensional Model Can Be Selected Because
  • It operates effectively with huge bulks of information almost in terabytes
  • It is highly scalable in nature
  • It supports major features like aggregations and actions that are not a part of the former

Good Read: Understanding SQL Server Integration Services (SSIS) In A Simple Way

SSAS Frequently Asked Questions (FAQS)

  • What is a cube in SSAS?

A cube in SSAS is also known as an OLAP/multidimensional cube. It is a data structure that is built, using OLAP databases, to allow near-instant data analysis.

  • Why do we use the cube in SSAS?

A cube in SSAS helps maintain data in the optimal form and gets faster information from various data sources. It is a multi-dimensional database especially mean for OLAP and data warehousing applications.

  • What is SSIS SSRS SSAS?

The trilogy in Microsoft stands for SQL Server Integration Services, SQL Server Reporting Services, and SQL Server Analysis Services.

  • What is the difference between Microsoft SSRS SSIS and SSAS?

SSRS is SQL Server Reporting Services, meant to showcase information in different formats. SSIS is SQL Server Integration Services, meant to perform the extraction, transformation, and loading of various types of data. SSAS is SQL Server Analysis Services, meant to analyze data based on OLAP, data mining, and BI elements.

  • What is the Multidimensional model in SSAS?

A multidimensional model in SSAS consists of various database components like measures, data sources, dimensions, aggregations, perspectives, etc.

  • How do I create a Tabular model in SSAS?

You need to choose File -> New -> Project in SSDT. Then, type the name in the integrated workspace and move ahead.

  • What is the difference between tabular and multidimensional SSAS?

The tabular SSAS looks at the representation of data through different tables whereas the multidimensional SSAS looks at the representation of data through OLAP cubes.

  • Why use SSAS Tabular model?

SSAS Tabular model is recommended because it acts faster on queries, is easy to perceive, and gives a good performance output.

  • Why use SSAS Multidimensional model?

SSAS Multidimensional model is recommended because it is highly effective with a large bulk of data, possesses higher scalability, and supports features like aggregations and actions.

  • What is the purpose of SSAS?

SSAS is meant for data mining and OLAP operations in Microsoft SQL Server. It is leveraged by businesses to perform in-depth analysis on data lying on disparate data sources.

  • Does the SSAS store data?

SSAS stores data and aggregations to offer faster performance. It processes information from the RDBMS into the cube.

  • Does Power BI replace SSAS?

No, Power BI and SSAS have different objectives. Power BI is more towards business intelligence outputs whereas SSAS looks mainly at the analytics portion of data.

  • Is SSAS an ETL tool?

No, SSAS is not an ETL tool. It looks at the analysis of data collected from various sources and their interpretation.

  • Is SSAS a data warehouse?

SSAS needs a data warehouse to refer to the data that is being analyzed. The cubes that are used in SSAS refer to these data warehouses.

  • What is the MDX Query in SSAS?

MDX Query in SSAS stands for Multidimensional Expressions. It is a query language that is used to get data from different multidimensional databases and query it from SSAS.

  • What are the steps to learn SSAS?

Learning the following in a specific order will help explore SSAS: Read data from various data sources and choose schema in SSDT. Develop an SSAS database. Deploy and process the database through the data model and then test and report the data accordingly.

  • How many types of dimensions are possible in SSAS?

Two dimensions are possible in SSAS – Database dimension and Cube dimension.

  • How do I create an OLAP cube in Excel?

From the Cubes pane, choose a cube name and then click Analyse Cube in Excel through Tasks. As soon as the worksheet opens in Excel, select the fields from the list, and create necessary charts and slicers/dicers.

As We Wrap Up

Amongst the trio, forming the fundamental base of the Microsoft BI family, SSAS forms the backbone and has a prime role to play when it comes to offering the best of data-driven insights into the deep sea of information. It is interesting to watch the world making the most of Microsoft’s trio legacy!

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