What is SSAS?

It is a Microsoft Business intelligence tool for creating Online analytical processing and data mining functionality. We can create an OLAP (online analytical processing)  cube using SQL server analysis system.

Key Features of SSAS:

  • Ease of use with many wizards and designers
  • Data model creation and management is flexible
  • OLAP handling from scalable architecture
  • Customize application from extensive support

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Why do we use SSAS:

  • Speed
  • Shared metadata
  • Security
  • Multidimensional analysis
  • Avoid resource contention with the source system
  • Consolidate from multiple sources

What is OLAP  Cube? Uses of OLAP Cube.

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It is a technology that stores the data in an optimized way to easily fetched from different types of complex queries by using various measures and dimensions. We can develop the OLAP cube  using the BIDS (business intelligence  development studio)

Follow the below steps to run the query in SSMS (SQL Server management studio)

  • Open SSMS 2008
  • Connect the database engine
  • Open new query editor
  • Paste the SQL script here
  • Press F5 to run the script
  • It will create and populate the “Sales_DW” database.

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OLAP Cube developing:

Step 1: Start a Business intelligence development studio environment.

Start menu –> Microsoft SQL Server 2008 R2  –>Select SQL Server BIDS

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Step 2: Start analysis services project: SSAS can create and occupy a physical table in the data source it will use to occupy the dimension maintained in the SSAS databases, On the specify source information.

 File–> New –> Project –> Business Intelligence projects –> select Analysis service project –> give  project name –> Click OK .

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Step 3: Creating a New data source
Right-click on Data source –> Click  New Data Source
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Click on the next new button. Create a new connection

  •  Select the SQL server name  where we created the data warehouse
  • Select server authentication mode
  • Enter your username and password  to connect SQL Serve
  • Select database Sales_DW
  • Test connection –> OK.

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Select Connection created in Data connections –>next –> select option Inherit –> next –> assign data source name –> finish

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Step 4: Creating a New data source view: We create a data source view (DSV) as an abstraction of the tables from the data source. In the solution explorer, right-click on the Data Source view then click new data source view to create the new one data source view, after that click on the next and select relational data source then next.
Right click on Data Source view –> New data Source view –> next –> Select Relational data source which we have created previously i.e., Sales_DW –> next

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shift Fact Table from available objects to Included objects

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Select FactProductSales  –> Add related tables –> next –> next –> Assign Name  (SalesDW DSV)  –> Finish
Now data Source view is ready to use.
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Step 5: Creating New Cube
In solution Explorer –> Cube (Right click) –> New cube –> Next –>.Select Use existing  tables –> Next –> FactProductSites –> Next –>Choose measure–>Next –> Select all the  Check box in select new dimensions –>Next –> Assign cube name –> Finish.
Now Cube is ready
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Step 6: In Solution Explorer, Click on dimension DimProduct à drag and drop the product name
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Step 7 :Deploy the Cube
In the Solution Explorer, right click on project name –> properties –> In configuration properties, Select Deployment –> Assign  SQL Server Instance Name –> Deploy All –> Do not process –> OK.
Right-click on Project Name –> Click Deploy
We can see the  message Deployment Completed in properties

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Step 8: Process the Cube: Right-click on Project Name –> Process

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Click on Run to process the cube after completing the process, we can see the status as process succeed –> close.
Step 9: Browse the cube for analysis
Right-click on the  cube name à browse
Follow the steps to browse our cube

  • Product Name Drag & Drop into Column
  • Full Date UK Drag & Drop into Row Field
  • FactProductSalesCount drag and drop this measure in Detail field

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