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In the ever-evolving world of digital data, savvy analysis has become not just valuable, but imperative. With Microsoft’s SQL Server Analysis Services (SSAS), we have the tool to process extensive data in a multidimensional way with increased speed, versatility, and understanding. This beginner’s guide will open the doors of SSAS and offer you a glimpse of the power behind SQL server analytics. Let’s begin.
What is SQL Server Analysis Services (SSAS)?
SSAS is a technology from Microsoft Corporation part of the Microsoft SQL Server suite, designed to analyze large amounts of data swiftly and generate reports. It’s an online analytical processing (OLAP), data mining and reporting tool used as a tool by organizations to analyze and make sense of information possibly spread out across multiple databases, or in disparate tables or files.
Why Use SSAS?
In a forward-thinking business environment, SSAS grants the capability to analyze and glean insights from vast amounts of data. This can lead to more informed decisions, improve productivity and drive business strategies. Now, how exactly do you use it?
How to Use SSAS?
Using SSAS starts by creating cubes via the SQL Server Business Intelligence Development Studio. These are multi-dimensional databases that collect data from different data sources into a single, easily manageable structure. Once the cube is formed, queries can be made against it for analysis.
Creating a Data Source
Creating a data source is our first step. A data source is an object that allows SSAS to connect to a database. Below is a sample SQL code result for creating a data source:
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CREATE DATASRC MyDataSource SET CONNECTION_STRING = 'Data Source=MyServer;Initial Catalog=MyDB;Provider=SQLNCLI10;Integrated Security=SSPI;' SET IMPERSONATION_INFO = USE_EXISTING |
This code creates a data source named “MyDataSource” that SSAS can use to connect to the database “MyDB” on the server “MyServer”.
Creating a Data Source View
Once the data source is set, we need to develop a Data Source View (DSV). This involves a schema of tables or views from the data source combined in a way that simplifies the metadata for our cube. Here is an example:
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CREATE DSV MyDSV FROM DATASRC MyDataSource ADD TABLE dbo.Products AS 'Products' ADD TABLE dbo.Orders AS 'Orders' ADD TABLE dbo.Customers AS 'Customers' |
This SQL code creates a DSV named “MyDSV” derived from our data source “MyDataSource” and includes the Products, Orders, and Customers tables from our database.
Creating a Cube
With our data source and view set, we are now ready to create a cube. The cube is the core component of SSAS, providing multi-faced viewing of the data to permit complex analytical and ad-hoc queries with fast execution times.
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CREATE CUBE SalesCube FROM DSV MyDSV ADD MEASURE 'Orders'.[Total Amount] ADD DIMENSION 'Products'.[Category] AS 'Category' ADD DIMENSION 'Customers'.[Region] AS 'Region' |
This code creates a cube named “SalesCube”, using our DSV “MyDSV”. The cube has a measure of the total amount from the Orders table, and two dimensions – Product Category and Customer Region, derived from the respective tables.
Querying a Cube
Finally, once the cube is formed, we can query it using the MDX query language. MDX is to a cube what SQL is to a relational database. The code to query our “SalesCube” for total sales per product category and by region would be:
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SELECT [Measures].[Total Amount] ON COLUMNS, [Region].[Region].Members ON ROWS FROM [SalesCube] WHERE [Category].[Category].&[Electronics] |
This code queries our cube for total sales amount by region, for the category ‘Electronics’.
Hopefully, this guide has given you a good starting point for SQL Server Analysis Services. Please be reminded that SSAS is an extensive platform with many advanced features not covered here. But with these basics, you are now well-equipped to start exploring and making sense of big data with SSAS.