SSAS Tutorial

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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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.

What is the history and evolution of SSAS?

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

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.

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.

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.

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..

Tableau 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.

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.

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How is the WHERE clause used in MDX queries?

The WHERE clause in MDX queries is used to filter the results based on specific conditions. It allows us to specify criteria that the data must meet in order to be included in the query's result set.

In MDX, the WHERE clause is typically used to restrict the dimensions or members that are included in the query. This is done by specifying the dimension and its members within the WHERE clause.

For example, let's consider a scenario where we have a cube with dimensions such as Product and Color, and a measure called Sales. To retrieve the sales data for products with a specific color, we can use the WHERE clause to filter the results.

The syntax of an MDX query with a WHERE clause would be:

SELECT [Measures].[Sales] ON COLUMNS,

    [Product].[Product Line].[Product Line].Members ON ROWS

FROM [ProductCube]

WHERE ([Product].[Color].[Silver])

In this example, we are querying the Sales measure on the Columns and the Product Line dimension on the Rows. The WHERE clause is used to specify that we want only the products with the color "Silver". This effectively filters the result set to include only the sales data for products with that particular color.

The result of the query would be a table with two columns, Product Line and Sales, and the corresponding sales data for the products with the color "Silver".

In summary, the WHERE clause in MDX queries is used to filter the dimensions or members based on specific conditions, allowing us to retrieve a subset of data that meets those conditions.

How is the FROM clause used in MDX queries?

The FROM clause in MDX queries is used to specify the cube from which data is to be retrieved. It functions similarly to the FROM clause in SQL queries that is used to identify the table. In an MDX query, the FROM clause is a necessary component and must be defined for each query.

The syntax of the FROM clause in an MDX query is as follows: FROM . Within the cube_expression, you need to specify the name of the cube from which you want to retrieve data.

Unlike SQL queries, which allow the use of multiple tables through the FROM clause, an MDX query only allows the use of a single cube. This means that you cannot combine data from multiple cubes in a single MDX query.

In summary, the FROM clause in MDX queries is essential for specifying the cube from which data is to be retrieved. It is similar to the FROM clause in SQL queries used to identify tables but differs in that MDX queries only allow the use of a single cube.

How is the SELECT statement used in MDX queries?

The SELECT statement in MDX queries is used to retrieve specific subsets of multidimensional data from a cube. It bears similarities to the SELECT statement in SQL, but with some key differences due to the analysis of multidimensional data.

In SQL, the SELECT statement allows you to specify which columns should be included or skipped when retrieving two-dimensional data. However, in MDX queries, multidimensional data is analyzed, meaning that there can be multiple axes involved. While two-dimensional data has X and Y axes, MDX queries provide the capability of retrieving data from one, two, or even more axes.

The basic syntax of the SELECT statement in an MDX query involves using axis expressions:

SELECT [, [axis_expression> ...]]

The axis expression is used to indicate the dimensions that you want to view or retrieve data from. These dimensions are known as axis dimensions because the data from these dimensions is projected onto the corresponding axis.

The syntax for an axis expression in MDX queries can be represented as:

 :=  ON (axis | AXIS (axis number) | axis number)

Within an MDX query, you can retrieve multi-dimensional result sets. A set is a collection of tuples that forms an axis dimension. This allows you to define up to 128 axes within the SELECT statement.

The first five axes are often given aliases such as ROW, COLUMN, SECTION, PAGE, CHAPTER, etc. However, axes can also be specified using numbers, which enables the selection of more than five dimensions in the query.

Overall, the SELECT statement in MDX queries provides the means to retrieve specific subsets of data from a multidimensional cube by specifying the desired axis expressions.

How are dimensions and sets used in MDX queries?

Dimensions play a fundamental role in MDX queries as they form the structure of a multidimensional database. Each cube in a multidimensional database contains one or more dimensions. In MDX, a dimension is a way to categorize or organize data. It represents a specific attribute or characteristic of the data being analyzed. For example, in a sales database, dimensions could include time, product, location, and customer.

Sets, on the other hand, are used to represent a collection of members from one or more dimensions. A set in MDX can contain zero, one, or multiple tuples. An empty set is used when there are no tuples to be included. Sets are defined within curly braces ({}) and can include specific members from a dimension or even cross-dimensional members. They allow for the selection and manipulation of specific data points within a cube.

In an MDX query, dimensions and sets are used together to retrieve and analyze data. The basic syntax of an MDX query includes clauses like SELECT, FROM, WHERE, and WITH. Dimensions are used in the SELECT clause to specify the attributes or members that should be included in the result set. They define the axes of the query such as rows, columns, and additional axes.

The FROM clause in an MDX query specifies the cube from which the data will be retrieved. It is similar to the FROM clause in SQL and is necessary to define for each MDX query.

The WHERE clause, in conjunction with the SLICER specification, allows for further filtering of the data. It helps in defining conditions based on dimensions and sets to retrieve specific portions of data from the cube. The SLICER dimension acts as a filter to remove unwanted members or dimensions, allowing for more precise data analysis.

In summary, dimensions provide the structure and categorization of data in a multidimensional database, while sets are used to represent collections of members from one or more dimensions. Dimensions and sets are then used together in MDX queries to select, analyze, and filter specific data points within a cube.

What are the best practices for using SSAS?

Define Aggregations Properly: It is essential to define aggregations efficiently to enhance the performance of your SSAS cubes. Identify the most frequently queried dimensions and measures and create appropriate aggregations to optimize query response time.

Optimize Cubes and Measure Group Design: Design your cubes and measure groups with performance in mind. Consider factors like hierarchies, relationships, and data types to ensure optimal cube processing and query performance.

Implement Partitioning: Partitioning is a useful technique to improve the query performance by dividing large datasets into manageable segments. Utilize partitioning strategies such as range or list partitioning to enhance processing and response time for specific segments of data.

Write Effective MDX Queries: MDX (Multidimensional Expressions) is the query language used in SSAS. To achieve optimal performance, write efficient MDX queries by applying best practices like avoiding unnecessary calculations, using proper filtering techniques, and utilizing the appropriate MDX functions.

Efficiently Use Query Engine Cache: The query engine cache in SSAS stores the results of previously executed queries. Make use of it by defining proper cache settings, caching frequently used queries, and tuning cache storage to minimize query processing time.

Consider Tabular Data Model Use: While SSAS provides both multidimensional and tabular data models, choose the tabular model only when it suits your specific requirements. Ensure that your data model aligns with the nature of your data and analytics needs before considering the tabular data model.

By following these best practices, you can enhance the performance and efficiency of your SSAS implementation, resulting in faster query response times and improved overall user experience.

How are MDX queries structured?

MDX queries are structured in a way that allows for the retrieval of data from multidimensional tables. The language provides a systematic approach for formulating queries that specifically target the desired information. In future sections, we will delve into a comprehensive exploration of MDX, covering its various components, syntax, and functionalities, to provide a thorough understanding of how MDX queries are structured.

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What is the difference between SSAS and PowerPivot?

SSAS and PowerPivot are two distinct business intelligence (BI) tools with notable differences. 

Features:

SSAS is primarily designed for corporate BI, while PowerPivot focuses on self-service BI. This means that SSAS is more suitable for large organizations with IT departments overseeing data analysis, whereas PowerPivot is geared towards individual users who prefer managing their own data analysis.

Deployment:

In terms of deployment, SSAS can be deployed to the SSAS server, while PowerPivot can be deployed to SharePoint. This indicates that SSAS is typically integrated into existing corporate infrastructure, while PowerPivot can be easily shared and accessed through SharePoint.

Uses:

SSAS utilizes Visual Studio projects for development and implementation, whereas PowerPivot leverages Excel projects. This means that developers accustomed to Visual Studio may find SSAS more familiar, while Excel users may prefer working with PowerPivot.

Size:

SSAS has limited memory capacity, whereas PowerPivot sets a specific limit of 2GB. Therefore, SSAS may be more suitable for handling larger datasets or complex analyses, while PowerPivot may have limitations when dealing with extensive data volumes.

Partition Support:

Another distinction is that SSAS supports partitioning, enabling more efficient data management and analysis. PowerPivot, on the other hand, lacks partitioning capabilities, which may affect its performance when handling large datasets.

Query Types:

When it comes to query types, SSAS supports both DirectQuery and Vertipaq, enabling users to query data directly from the data source or analyze in-memory data. In contrast, PowerPivot only allows Vertipaq queries, restricting users to analyzing in-memory data.

Admin Tools:

SSAS provides server administration tools, allowing IT departments to manage the server and its functionalities. In contrast, PowerPivot offers Excel and SharePoint admin tools, which are primarily focused on managing workbooks and controlling access in a more user-centric manner.

Security:

SSAS offers dynamic security levels, enabling administrators to implement role-based access control and define granular data access permissions. In contrast, PowerPivot relies on workbook file security, which means security is embedded within the Excel workbook and may not provide the same level of control as SSAS.

In summary, SSAS and PowerPivot differ in their focus, deployment options, development environments, memory capacity, partitioning support, query types, admin tools, and security features. Understanding these differences can help determine which tool best suits the specific needs and requirements of an organization or individual user.

What is the difference between tabular and multidimensional data models in SSAS?

What is the size difference between tabular and multidimensional data models?

The tabular model is small in size, while the multidimensional model is larger in size.

What is the complexity difference between tabular and multidimensional data models?

The tabular model is simple, while the multidimensional model is complex.

What are the best features of the tabular and multidimensional data models?

The tabular model eliminates the need to move data from the source and works best when data is stored into a star schema. The multidimensional model is effective for measuring values rolled up within hierarchies and computing values in a sparse cube. 

What is the structure difference between tabular and multidimensional data models?

The tabular model has a loose structure, while the multidimensional model has a rigid structure.

What is the storage difference between tabular and multidimensional data models?

The tabular data model uses in-memory storage, while the multidimensional data model uses file-based storage.

What is the development time difference between tabular and multidimensional data models?

The development time is very less with tabular data models because related tables can serve both roles.

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.

What are the key features of SSAS?

Can SSAS be accessed remotely?

Yes, SSAS can be accessed anytime and anywhere as long as there is an internet connection available. This allows for remote accessibility of the tool.

How does SSAS keep organizational data secure?

SSAS provides powerful solutions to keep organizational data secure, ensuring that sensitive information remains protected. 

What are the requirements to use SSAS?

To use SSAS, all you need is a normal internet connection and a web browser. There is no additional software requirement.

How does SSAS ensure data integrity?

SSAS ensures data integrity through automatic data backups, which helps in safeguarding the organizational data. 

What are the advantages of SSAS in terms of data analysis?

SSAS offers the advantage of shorter query response time through facts aggregation, resulting in faster data analysis.

What are some important SSAS terminologies?

What is MDX and how is it used in SSAS?

MDX, which stands for Multidimensional Expressions, is a query language used to retrieve data from multidimensional tables in SSAS. It allows users to perform complex calculations, aggregations, and filtering on the dimensions and measures of a cube. MDX queries are written in a specific syntax and can be used to analyze and manipulate data within SSAS. 

What is a Data Source View in SSAS?

A Data Source View in SSAS is a logical model of the data source. It provides additional information about how the data is structured and related, acting as a connection between the analysis database and the underlying relational database management system (RDBMS). 

What is a Level in SSAS?

In SSAS, a Level is a type of summary that can be retrieved from a single dimension. It provides a way to organize and aggregate data within a dimension hierarchy. 

What is an OLAP and what does it consist of?

OLAP stands for Online Analytical Processing and it consists of data cubes that contain measures and dimensions. These cubes include almost all members in a hierarchical relationship and follow a specific set of rules to compute specific cells in a sparse cube.

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.

What are the drawbacks of using SSAS?

What are the risks associated with the tabular data model if project requirements change?

The tabular data model may be risky if project requirements change mid-way through the project.

Is it possible to merge data between Tabular and Multidimensional cubes?

It is not possible to merge data between Tabular or multidimensional cubes.

Can the data model be changed to other versions?

Once you have decided on a data model either Tabular or Multidimensional, you cannot change it to other versions.

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.