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SQL Server 2017 - SSAS Multidimensional Databases

( Duration: 3 Days )

The SQL Server 2017 - SSAS Multidimensional Databases training course provides skills to administer, design, and build multidimensional databases using SQL Server Analysis Services (SSAS).

By attending SQL Server 2017 - SSAS Multidimensional Databases workshop, delegates will learn to:

  • Describe the components, architecture, and nature of a BI solution
  • Implement a logical design for a data warehouse
  • Create a multidimensional database with analysis services
  • Implement dimensions in a cube
  • Implement measures and measure groups in a cube
  • Use MDX syntax
  • Customize a cube
  • Use data mining for predictive analysis

  • At least 2 years' experience of working with relational databases
  • Designing a normalized database
  • Querying with Transact-SQL is helpful but is not required



Introduction to Business Intelligence and Data Modeling

  • Introduction to Business Intelligence
  • The Microsoft business intelligence platform

Designing and Implementing a Data Warehouse

  • Logical Design for a Data Warehouse
  • Physical Design for a Data Warehouse

Creating Multidimensional Databases

  • Introduction to multidimensional analysis
  • Creating data sources and data source views
  • Creating a cube
  • Overview of cube security
  • Configure SSAS
  • Monitoring SSAS

Working with Cubes and Dimensions

  • Configuring dimensions
  • Define attribute hierarchies
  • Sorting and grouping attributes
  • Slowly Changing Dimensions

Working with Measures and Measure Groups

  • Working with measures
  • Working with measure groups

Introduction to MDX

  • MDX fundamentals
  • Adding calculations to a cube
  • Using MDX to query a cube

Customizing Cube Functionality

  • Introduction to Business Intelligence
  • Implementing key performance indicators
  • Implementing actions
  • Implementing perspectives
  • Implementing translations

Performing Predictive Analysis with Data Mining

  • Overview of Data Mining
  • Creating a Custom Data Mining Solution
  • Validating a Data Mining Model
  • Connecting to and Consuming a Data-Mining Model
  • Using the Data Mining add-in for Excel

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