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Developing SQL Data Models

  • Course Code:

    20768A
  • Course Duration:

    3 Days
  • Course Price:

    $2505
  • Availability:

    Call/Email to register interest
  • Overview & ObjectivesOpen or Close

    About this course

    This three-day instructor-led course is aimed at database professionals who fulfil a Business Intelligence (BI) developer role. This course looks at implementing multidimensional databases by using SQL Server Analysis Services (SSAS), and at creating tabular semantic data models for analysis with SSAS.

    Audience profile

    The primary audience for this course are database professionals who need to fulfil BI Developer role to create enterprise BI solutions. Primary responsibilities will include:

    • Implementing multidimensional databases by using SQL Server Analysis Services
    • Creating tabular semantic data models for analysis by using SQL Server Analysis Services
    • The secondary audiences for this course are ‘power’ information workers/data analysts
  • Skills GainedOpen or Close

    After completing this course, students will be able to:

    • Describe the components, architecture, and nature of a BI solution
    • 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
    • Implement a tabular database
    • Use DAX to query a tabular model
    • Use data mining for predictive analysis
  • Course ModulesOpen or Close

     

    Course Outline

    Module 1: Introduction to Business Intelligence and Data Modeling

    This module introduces key BI concepts and the Microsoft BI product suite.

    • Introduction to Business Intelligence
    • The Microsoft business intelligence platform

    Lab : Exploring a Data WarehouseAfter completing this module, you will be able to:

    • Describe the concept of business intelligence
    • Describe the Microsoft business intelligence platform

    Module 2: Creating Multidimensional Databases

    This module describes the steps required to create a multidimensional database with analysis services.

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

    Lab : Creating a multidimensional databaseAfter completing this module, you will be able to:

    • Use multidimensional analysis
    • Create data sources and data source views
    • Create a cube
    • Describe cube security

    Module 3: Working with Cubes and Dimensions

    This module describes how to implement dimensions in a cube.

    • Configuring dimensions
    • Define attribute hierarchies
    • Sorting and grouping attributes

    Lab : Working with Cubes and DimensionsAfter completing this module, you will be able to:

    • Configure dimensions
    • Define attribute hierarchies.
    • Sort and group attributes

    Module 4: Working with Measures and Measure Groups

    This module describes how to implement measures and measure groups in a cube.

    • Working with measures
    • Working with measure groups

    Lab : Configuring Measures and Measure GroupsAfter completing this module, you will be able to:

    • Work with measures
    • Work with measure groups

    Module 5: Introduction to MDX

    This module describes the MDX syntax and how to use MDX.

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

    Lab : Using MDXAfter completing this module, you will be able to:

    • Describe the fundamentals of MDX
    • Add calculations to a cube
    • Query a cube using MDX

    Module 6: Customizing Cube Functionality

    This module describes how to customize a cube.

    • Implementing key performance indicators
    • Implementing actions
    • Implementing perspectives
    • Implementing translations

    Lab : Customizing a CubeAfter completing this module, you will be able to:

    • Implement key performance indicators
    • Implement actions
    • Implement perspectives
    • Implement translations

    Module 7: Implementing a Tabular Data Model by Using Analysis Services

    This module describes how to implement a tabular data model in PowerPivot.

    • Introduction to tabular data models
    • Creating a tabular data model
    • Using an analysis services tabular model in an enterprise BI solution

    Lab : Working with an Analysis services tabular data modelAfter completing this module, you will be able to:

    • Describe tabular data models
    • Create a tabular data model
    • Be able to use an analysis services tabular data model in an enterprise BI solution

    Module 8: Introduction to Data Analysis Expression (DAX)

    This module describes how to use DAX to create measures and calculated columns in a tabular data model.

    • DAX fundamentals
    • Using DAX to create calculated columns and measures in a tabular data model

    Lab : Creating Calculated Columns and Measures by using DAXAfter completing this module, you will be able to:

    • Describe the fundamentals of DAX
    • Use DAX to create calculated columns and measures in a tabular data model

    Module 9: Performing Predictive Analysis with Data Mining

    This module describes how to use data mining for predictive analysis.

    • Overview of data mining
    • Using the data mining add-in for Excel
    • Creating a custom data mining solution
    • Validating a data mining model
    • Connecting to and consuming a data mining model

    Lab : Perform Predictive Analysis with Data MiningAfter completing this module, you will be able to:

    • Describe data mining
    • Use the data mining add-in for Excel
    • Create a custom data mining solution
    • Validate a data mining solution
    • Connect to and consume a data mining solution
  • PrerequisitesOpen or Close

    This course requires that you meet the following prerequisites:

    • Basic knowledge of the Microsoft Windows operating system and its core functionality.
    • Working knowledge of Transact-SQL.
    • Working knowledge of relational databases.
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