SSAS Tutorial - A Complete Guide

As Technology runs around the data, data is increasing enormously. To store this data,  enterprises are using data warehouses. When they have to make decisions, this data must be analyzed. To analyze this vast amount of data, you need a data analytic tool that uses a multi-dimensional approach.

That tool is SSAS.  SSAS is one of the MSBI(Microsoft Business Intelligence) suite tools that allow us to perform data analysis across multiple dimensions. SSAS is generally an analytical processing engine in the field of business intelligence. This SSAS tutorial helps you with all the basic concepts of SSAS and gives you a clear understanding of working with SSAS.

SSAS Tutorial - Table of Contents

What is SSAS? History of SSAS Why SSAS? Features of SSAS SSAS Architecture SSAS Terminology Types of Models in SSAS Languages used in SSAS Advantages of SSAS Disadvantages of SSAS SSAS FAQs Conclusion

What is SSAS?

SSAS refers to SQL server analysis services and is a booming technology designed to perform data mining and online analytical processing for operations related to Business intelligence. In simple terms, SSAS is an analytical engine that helps to divide and perform data analysis for massive volumes of data. It also provides accurate outcomes to make precise business decisions. There is increased growth and demand for SSAS, which also helps in cube creation.

SSAS is a popular tool that is capable of providing in-depth data analysis. The SSAS tool will provide you with adequate information from the higher volumes of data. SSAS offers multi-dimensional analysis and also involves efficient schema configuration.

If you want to enrich your career and become a professional in SSAS, then enroll in "SSAS Training" - This course will help you to achieve excellence in this domain.

History of SSAS

The SSAS technology has been available since ancient times and has become popular with the improvements and versions. Let us get to know more about the history of SSAS and its involvement in the current era.

• Microsoft, a reputed organization, acquired the OLAP software from panorama in 1996.
• Later, after two years, Microsoft took a chance to include OLAP software in the SQL server 7 and announced the release.
• Two years later, Microsoft took a chance to include data mining which this led to the transformation of the OLAP services into the Ms. SQL server analysis services in 2000. Increased flexibility is one of the key advantages of this implementation.
• Microsoft then released the SSAS 2005 in the year 2005 with an additional functionality called backward functionality, marking it as the evolutionary release during that time.
• Later on, many versions were released like SSAS 2008, SSAS 2014, SSAS 2012, and SSAS 2016 with the key features of scalability and good query performance.

Why SSAS?

Generally, organizations hold massive volumes of confidential data that require high security. Also, there is a need where all the required information needs to be retrieved from large volumes of data. The data will be stored in the databases and fetching can be done by running the commands and queries. SSAS is one of the technologies in which we can do anything with the data and helps in the retrieval and data management functionalities.

Let us know some of the key factors that SSAS provides and marked itself as essential in the current world.

1. Security: As the users are allowed to handle large volumes of data, security is one of the key factors to focus on. SSAS maintains security by setting up the permissions to the users for their roles, for accessing and analyzing the data as and when required.

2. Performance and scalability: It is important that the delivery of the outcomes should be faster. Business intelligence completely deals with the data and its analysis and requires high scalability and performance. Within a short span of time, the outcome should come up once the queries are applied to data sets.

3. Version Control: Now-a-days, it has become difficult for organizations to meet the KPI values, even though they maintain a reporting and monitoring tool. SSAS technology helps in defining and determining the targets and their formats, along with the descriptions to gain better understanding.

4. Usability: Everyone within the organization like the stakeholders, users, clients, and developers will need to work on the large volumes of data in different systems which may lead to incorrect outcomes. SSAS provides the flexibility to use and fetch the required data in a small amount of time with the correct results.

Features of SSAS

 Features in each technology are the source to prove its significance. SSAS has come up with unique features which are explained below.

• Speed: SSAS is capable of performing data management and data analysis by utilizing the cubes in a short span of time.
• Data Analysis: SSAS helps in performing the data analysis quickly with the aggregation results which are derived in a short span itself.
• Automatic Link and Display: SSAS is capable of providing extensive support by linking and displaying the results automatically without any manual intervention for the creation of the results.
• Good Data Model: SSAS is capable of providing the best data models for performing the data analysis in a better way and also maintaining the reporting strategies.

SSAS Architecture

To gain an understanding of how the SSAS tool works, it is essential to learn SsAS architecture. The SSAS tool holds a three-tier based architecture that includes three main components – RDMS, client, and SSAS. Let us gain a clear idea of each of the components now.

1. RDBMS (Relational Database Management System)

All the data from multiple resources like excel, databases, text, and other sources are pulled with the help of the ETL tool into the relational database management system. The relational database management system – RDBMS is responsible for collecting data from a different sets of resources.

2. SSAS( SQL Server Analysis Services)

 In this stage, all the collective data from the relational database management system will be moved to the cubes by using the project services. These cubes in SSAS will be creating a new database that includes analysis. This type of database is known as an analysis database which can be utilized for different purposes. 

Related Article: How to Create Cube in SSAS with Example

3. CLIENTS

The stakeholders or clients will now have the ability to access the data and also perform the operations as needed. The data access and management can be through the dashboards, portals, etc.

SSAS uses the client-server architecture, hence it is important for you to have an idea of the client and server architecture as well for better knowledge and understanding. Let us learn more about the client architecture and server architecture in detail.

4. Server Architecture

In SSAS, the Msmdsrv.exe application is used as the client component which basically runs in window service. It includes a query processor which performs the below set of tasks. It also maintains security which is one of the key factors needed.

• Metadata management
• Handling of the transactions
• Parse the statements that are received from the clients
• Perform the calculations
• Creation of aggregations and query scheduling
• Caching objects
• Server resources management 

 5. Client Architecture 

The server is capable of handling all the queries and functionalities. There is a need for a connection to be established between the server and the client for each request, and this communication can be established using the SOAP packets. The SSAS technology also provides its esteemed support to web services as well.

In the client architecture, all the data that is available from multiple resources like excel, text, database, access, and other sets of data will go through the OLAP processing. The cubes will be created, and the data can be accessed through dashboards, portals, reports, etc.

SSAS Terminology

• Cube: A cube is generally referred to as a multi-dimensional data structure,  a storage unit. It includes all the data collected which has to reflect when a query is raised. To perform data analysis, it makes use of the relations and dimensions and is considered one of the important components in SSAS.
• Dimensions: Dimensions specifically are used for defining the criteria to perform the analysis of data. It includes the collection of the attributes which are based on columns in the table.
• Dimension Table: The table in which multiple dimensions are listed is called a dimension table. The attributes define the characteristics of the fat and hold one or more relationships.
• Fact Table: A fact table includes the numerical data called facts which help in identifying the facts or history. This table helps in measuring the business of the organization. It also includes the measurements and keys or facts to the dimension table.
• Schema: The organization of the data in the DBMS – database management system is represented in the schema. It also includes the structure, which is represented in an understandable and formal language. Snowflake schema and star schema are two different types of schema.
• Measures: The columns in the table are called measures. Every fact table includes one or more fact tables that require proper analysis. In simple terms, the columns in the table are analyzed.
• Data Source: The source of the data, which is generally a connection between a relational database management system and an analysis database, is called the data source.

Types of Models in SSAS

Till now, you had an idea of the architecture and the terminologies used in the SSAS platform. Now it is time to know about the different types of models in SSAS. SSAS includes two types of models 

Let us learn more about these models in detail.

1. Multi-Dimensional Model

Cubes are included in the multi-dimensional models. The cubes hold the data and the operations to be performed. It helps in finding the cell value based on the query type by making use of the dimension numbers and cube as coordinates. The complexity level is high and also uses the file based memory.

2. Tabular Model

The tables are used to represent the data in the tabular model. The tables do not hold any dimensions like the cube in the mult-dimensional model. The tabular model makes use of the memory cache with low complexity. It is considered as the relational model as relational databases uses the tabular models and tables. 

Languages Used in SSAS

SSAS makes use of three different languages. Let us know in detail about the languages used in SSAS.

• MDX (Multi-dimensional Expressions): The MDX language is a query-based language that is an extension of the SQL language. It is specifically designed for the OLAP databases. The SSAS cubes will be used for data retrieval. To access the data in the SSAS cubes, the MDX Queries are used.
• DMX (Data Mining Extensions): DMX language helps in creating and working with the data mining models available in the SSAS service. The DMX language includes multiple statements like Data definition language (DDL) statements, Data manipulation language (DML) statements, operators, functions, and many more.
• XMLA (XML For Analysis): XMLA language helps in performing administration-related tasks. The tasks can be anything like moving, backup or copying a database.XMLA language is used for establishing communication between the server and client connections.

Advantages of SSAS

• The data analysis has become easier through SSAS service and is considered as the best tool with high compatibility and speed to perform numerical analysis.
• SSAS provides a clear view of the information specific to the organization along with the reporting analysis.
• SSAS allows avoiding resource contention.
• Whenever a query is raised, SSAS provides accurate results and utilizes cubes for the data management purposes.

Disadvantages of SSAS

• There is no possibility to make changes to the versions in SSAS. After you have decided to go with one of the models among the multi-dimensional model and tabular model, then only the version changes can be made.
• Merging is not possible in the multi-dimensional and the tabular cubes.
• There is a chance that the requirements might change during the implementation part of the project. It is risky if the tabular model is selected whenever there is a change in the requirements.

SSAS FAQ’s

1. What are the steps to learn SSAS?

If you are a beginner and would like to learn SSAS, then you need to follow the below steps.

• Learn programming languages like Python, R, and SSAS specific languages like MDX, DAX, etc.
• Explore resources online
• Undergo training and certification through a certified training provider
• Practice data analysis, go through live projects for real-time exposure. 

2. What is SSAS used for?

SSAS is an OLAP processing, data mining, and reporting tool to perform data analysis. It is an analytics engine that helps you manage large volumes of data – slice and dice the vast data, allowing analysis across various dimensions. 

Related Article: SSAS Interview Questions

3. Is SSAS an ETL Tool?

Yes, SSAS is an ETL tool that helps in achieving data integration and is a part of business intelligence.

4. What is an alternative for SSAS?

Kyvos is one of the best solutions for SSAS which helps you scale the OLAP cubes on the on-premise data lake, the cloud, or both.

 5. Is SSAS an OLAP?

In simple terms, SSAS is a BI tool that helps you create OLAP and data mining functionalities. It is possible to create the OLAP cubes using the SSAS service.

6. Which processing type is used in SSAS?

There are three different processing types for each entity – database, table, and proportions. The processing options are

• Database: Process Full, Process Default, Process clear, process data, Process Recalc.
• Table: Process Full, Process Default, Process clear, process data, Process defrag
• Partition: Process Full, Process Default, Process clear, process data, Process Add

Conclusion

SSAS is one of the booming technologies which has massive demand these days. SSAS is mainly used for analyzing the data stored in the OLAP server. As you now have an idea of its architecture, data models, and languages, you can install it and start working with it. I hope this SSAS tutorial is sufficient for you to get started with SSAS and explore it. If you’re looking forward to learning and master in SSAS, then enroll in this SSAS Course by MindMajix today.