SSAS Tutorials

As every technology is booming, we should be aware of the latest technologies and tools. Technology runs around data, and all business organizations would like to analyze and improve their business growth by running some analyses and reports. Analytics plays a vital role to make perfect decisions. Businesses choose different processes to analyze and make better decisions that will help in growth. To develop and improve the Online Analytical processing and data mining, Microsoft has come up with a technology called SQL Server Analysis Services(SSAS). In this tutorial, you will learn the basic concepts of SSAS and its importance.

What is SSAS?

SSAS means SQL Server Analysis Services.SSAS is a technology developed by Microsoft Business Intelligence to perform Online Analytical Processing and Data mining for Business Intelligence operations or applications.It is an analytical engine that will help us to divide and analyze large volumes of data. Indeed, it proves that we can play with the data and make business decisions correctly. 

SSAS is extensively the best tool which provides a faster and more in-depth analysis of data. When a query is applied, the SSAS tool helps in retrieving the adequate information from large volumes of data from various dimensions. The demand for SSAS is improving. SSAS helps in data analysis, cube creation using different dimensions. It is a multidimensional OLAP server and analytical engine which allows driving accurate results to analyze for better business decisions. 

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HISTORY OF SSAS

SSAS has been from olden times with a change in the versions and improvements. Let us know where SSAS has come up evolving.

  • The first step by Microsoft was in 1996, wherein Microsoft has acquired OLAP software technology from Panorama
  • In 1998, two years later, Microsoft had included OLAP software in SQL Server 7 and released the same 
  • In 2000, Microsoft again included Data mining and transformed OLAP services to Microsoft SQL Server Analysis Services 2000. This version has come up with improved flexibility.
  • In 2005, Microsoft released SQL Server Analysis Services 2005, which has come up with the backward functionality concept, which is an evolutionary release.
  • The versions SSAS 2008R2, SSAS 2012, SSAS 2014, 2016 have come up with the best query performance and scalability

WHY SSAS?

An organization or business holds large volumes of data. Many instances come up where we need to retrieve the required information from the massive data. Relational Databases are where the data is stored and fetched by applying some queries and commands. However, with SSAS, we can play with the data. It's indeed the best tool and technology that helps in retrieving and managing data with more in-depth analysis.

Let's see some of the reasons why we need SSAS.

  • Usability: Many of the business users, Business intelligence stakeholders, clients, developers will need to work with large tables in multiple systems by implementing logic and also will lead to many incorrect results. With SSAS, we can utilize and browse the required data in less time and with accurate results
  • Security: It's good to know that the users can access the large volume of data. The security lies in SSAS by allowing permissions based on their roles and security permissions to access and analyze the data.
  • Version control: Many of the organizations will fail to meet their KPI values though they have one visualization or reporting tool. SSAS helps in determining and defining the targets, formats, and descriptions to get a better understanding.
  • Performance and scalability: As Business Intelligence entirely deals with data and its analysis, it is essential to deliver the results faster. To be more specific, once queries are applied, the data should be available within a short span from the multiple data sets.

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FEATURES OF SSAS

SSAS has come up with many flexible and best features that are required in Business Intelligence and IT industries as well. Let us discuss some of the features of SSAS.

  • Speed: SSAS helps in managing and analyzing data in a shorter span using cubes
  • Data Analysis: Helps in analyzing the data quickly with more in-depth checks and fast responses
  • Automatic displays: Helps in displaying the analysis and reporting automatically
  • Data models: Capable of generating different data models for better analysis and reporting strategies
  • No software: With the usage of SSAS, there is no software required to perform any function.
  • Secured and accessible: The data is secured and available everywhere with an internet connection.

ARCHITECTURE OF SSAS

Now that we are aware of the features of SSAS, it is time to know about the architecture of SSAS.SSAS uses both client and server components to perform and deliver OLAP and data mining functionalities. The architecture of SSAS consists of three tiers:

RDBMS (Relational Database Management System): Data from different sources like database, text, excel, and many other sources will be taken or pulled up with the help of the ETL tool into the RDBMS. It is a collection of data from all the multiple sources.

SSAS( SQL Server Analysis Services): All the aggregate data from RDBMS will move into SSAS cubes through project services. These SSAS cubes will create a different database which includes the analysis and hence called an analysis database. Once an analysis database is available, used for multiple purposes.

CLIENTS: Clients or stakeholders will now be able to access the data and perform their activities. They can access and manage the data in the form of a portal, dashboards, etc.

As SSAS is a client server-based architecture, we will need to know the Client Architecture and Server Architecture as well.

SERVER ARCHITECTURE:

The Msmdsrv.exe is the application that uses as a client component. It runs as a window service and consists of security, query processor and performs the following tasks.

  • Parsing statements received from clients
  • Managing metadata
  • Handling the transactions
  • Processing calculations
  • Storage dimensions and cell data
  • Creating aggregations and scheduling queries
  • Caching objects
  • Managing server resources

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CLIENT ARCHITECTURE:

The Server itself will resolve all the functionalities and queries. Server to client connection is needed for every request raised. The communication happens using SOAP packets. SSAS provides its esteem support to web services as well.

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This diagrammatic representation helps us to understand how SSAS works. All forms of data from various sources like Database, text, access, excel, and other data will undergo through OLAP and DW by creating cubes and rendering the information in the form of portals, dashboards, reports, etc.

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TERMINOLOGIES USED IN SSAS

CUBE: A cube is a storage unit in SSAS. It is a multidimensional data structure that consists of the aggregated data that should reflect whenever there is a query request. It uses a set of dimensions and relations to analyze the data. It is one of the essential components in the SSAS multidimensional model.

DIMENSIONS: Dimensions define the criteria for analysis. It is a collection of attributes based on the columns specified in a table—the requirements of study based on time, geography to perform any function.

DIMENSION TABLE: Dimension table consists of the list of the dimensions based on which an action is performed. The characteristics of the fact are defined with the help of the attributes. They can include one or more relationships.

FACT TABLE: It is one of the most important tables in a dimensional model that will measure the business of an organization. It consists of numerical data called facts which helps us determine the history or facts. It contains foreign keys and measurements or facts to the dimension table.

SCHEMA: Schema represents the organization of the data in the database management system. It includes the structure written in a formal and understandable language. There are two types of schema called Star schema and Snow-flake schema.

MEASURES: Measures refer to the columns in the table. Each fact table consists of one or more fact tables which need to be appropriately analyzed. It is nothing but the columns of a fact table that needs to be analyzed.

DATA SOURCE: Data source is nothing but the source of data which is a connection between the analysis database and RDMS. It is a connection string.

frequently asked SSAS interview questions for freshers & experienced professionals

TYPES OF MODELS IN SSAS

SSAS includes two different types of models: The multidimensional model and the Tabular Model. Let us know more information about each model in specific.

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MULTI-DIMENSIONAL MODEL:

The SSAS multidimensional model includes cubes. The data, organized in the form of cubes which includes the operations to perform. It specifically allows you to find the value of the cells in the type of a query by using cube and dimension numbers as the coordinates. It also decides the specific way like the values should be measured and the value computation in a sparse cube. They used file-based memory with a rigid structure. The size of this model data will be more extensive, and the complexity will be more..

TABULAR MODEL:

In the tabular model of SSAS, the data is represented in the form of tables. The tables do not have the dimensions as in the cube in the multidimensional model. It uses a memory cache with a loose structure. It is a relational model as relational tables use tabular models. The data size is smaller, and the complexity is low.

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CUBES AND CREATION OF CUBES IN SSAS:

Cubes are multidimensional data structures that store data in the form of cubes using facts and dimensions. A Cube in SSAS is used for faster and more in-depth analysis of data. Data is stored in cubes for dividing large volumes of data which will help in retrieving the accurate data in a short period.In short, the cubes are used in SSAS to organize the data so that accessing and retrieving of required data based on the query will be easy.

Steps to be followed to create an OLAP Cube in SSAS:

  • A data warehouse is created in the Microsoft SQL Server studio.
  • Create a new analysis service project in the Business Intelligence Development Studio, 
  • Create a new data source by using right-click on the data sources option in Solution Explorer.
  • Choose connections button or create a new connection followed by the next button
  • Select Inherit->Next->Finish.This step will create the data source.
  • Right-click on the data source view option in Solution Explorer.
  • Click on Next-> Select the data source->Click on Next.
  • Move the fact table onto the right pane and click on the Add related tables button.
  • Click next and enter the data source view name followed by the Finish button. This will create the Data Source View 
  • Create a new cube in Solution explorer by using right-click on Cubes.
  • Click on Next->next->Select the fact table->Select the measures-> Next
  • Select the dimension table-> Enter cube name followed by Finish. This step will complete the creation of the cube in SSAS.

LANGUAGES USED IN SSAS

Let us have a quick review of the languages used in SSAS. SSAS uses three different languages as listed below:

  • MDX (Multidimensional Expressions): MDX is a query language used in SSAS which is designed for OLAP databases. MDX language is an extension of SQL Language where the data is retrieved from the SSAS cubes. It allows queries and access to the multidimensional data.MDX language is found to be different in terms of implementations and conceptualization.MDX queries are used to access the data in the Server Analysis Service Cube.
  • DMX (Data Mining Extensions): DMX is the language used to create and work with the Data Mining models in SQL Server Analysis Service.DMX language includes DDL (Data Definition Language) statements, DML(Data Manipulation Language) statements, functions, and operators as well.
  • XMLA (XML For Analysis): XMLA is the language that is mainly used to perform administrative tasks. These tasks include restore and backup, move and copy the database, and many more. XMLA is a protocol used to establish communications between the client and server connections.

ADVANTAGES OF SSAS:

  • Analysis of data is made easy through SSAS. It is considered as an ideal tool for numerical analysis.
    Speed and compatibility is high.
  • Gives a unified and structured view of all the information related to the organization, which includes reporting analysis.
  • SSAS provides accurate data when a query is placed as it uses cubes for managing the data.
  • Resource contention can be avoided using SSAS.

DISADVANTAGES OF SSAS:

  • The versions cannot be changed. Once you have decided to go through using one particular model, either Tabular or Multidimensional, the version can be changed.
    Merging is not allowed between the tabular and multidimensional cubes.
  • The requirements might change during the project implementation process. The tabular data model is found to be risky if the requirements change during the mid-way of the project.

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CONCLUSION

SSAS plays a prominent role in Business Intelligence to analyze and implement the functionalities of a business organization. Now that you have an idea of the basic concepts about the features, pros, and cons of using SSAS, SSAS models, you will need to learn more deeply to gain in-depth knowledge in SSAS. I hope the information in the tutorial is helpful. Assuming that, it has given a basic idea, I would like to keep you informed that learning SSAS is a plus in Business Intelligence.I hope you have got an insight regarding SSAS. For any queries/questions related to the topic, we are here to help you in resolving them. 

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Mudassir
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Mudaasir is a programming developer for hkr trainings. He has a well knowledge of today’s technology and I’ve loved technology my entire life. And also been lucky enough to work for the programmer including science and technology. Big thanks to everyone who has followed me on LinkedIn and twitter.

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