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.
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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SSAS has been from olden times with a change in the versions and improvements. Let us know where SSAS has come up evolving.
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.
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.
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.
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.
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.
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
SSAS includes two different types of models: The multidimensional model and the Tabular Model. Let us know more information about each model in specific.
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..
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.
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:
Let us have a quick review of the languages used in SSAS. SSAS uses three different languages as listed below:
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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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