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:
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.
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 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:
SSAS is popular owing to the multiple benefits that it yields, some of them are as listed below:
|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|
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.
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.
The trilogy in Microsoft stands for SQL Server Integration Services, SQL Server Reporting Services, and SQL Server Analysis Services.
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.
A multidimensional model in SSAS consists of various database components like measures, data sources, dimensions, aggregations, perspectives, etc.
You need to choose File -> New -> Project in SSDT. Then, type the name in the integrated workspace and move ahead.
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.
SSAS Tabular model is recommended because it acts faster on queries, is easy to perceive, and gives a good performance output.
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.
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.
SSAS stores data and aggregations to offer faster performance. It processes information from the RDBMS into the cube.
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.
No, SSAS is not an ETL tool. It looks at the analysis of data collected from various sources and their interpretation.
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.
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.
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.
Two dimensions are possible in SSAS – Database dimension and Cube dimension.
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.
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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