Developing SQL Data Models

Introduction

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

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.

Prerequisites

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

At course completion

After completing this course, participants 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

Exams

This course prepares to the exam 70-768 Developing SQL Data Models.

Course outline

Module 1: Introduction to Business Intelligence and Data Modeling

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

  • Lesson 1: Introduction to Business Intelligence
  • Lesson 2: The Microsoft business intelligence platform

Module 2: Creating Multidimensional Databases

This module describes how to create multidimensional databases using SQL Server Analysis Services.
 

  • Lesson 1: Introduction to Multidimensional Analysis
  • Lesson 2: Data Sources and Data Source Views
  • Lesson 3: Cubes
  • Lesson 4: Overview of Cube Security
  • Lesson 5: Configure SSAS
  • Lesson 6: Monitoring SSAS

Module 3: Working with Cubes and Dimensions

This module describes how to implement dimensions in a cube.
 

  • Lesson 1: Configuring Dimensions
  • Lesson 2: Defining Attribute Hierarchies
  • Lesson 3: Implementing Sorting and Grouping Attributes
  • Lesson 4: Slowly Changing Dimensions

Module 4: Working with Measures and Measure Groups

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

  • Lesson 1: Working with Measures
  • Lesson 2: Working with Measure Groups

Module 5: Introduction to MDX

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

 
  • Lesson 1: MDX fundamentals
  • Lesson 2: Adding Calculations to a Cube
  • Lesson 3: Using MDX to Query a Cube

Module 6: Customizing Cube Functionality

 
  • Lesson 1: Implementing Key Performance Indicators
  • Lesson 2: Implementing Actions
  • Lesson 3: Implementing Perspectives
  • Lesson 4: Implementing Translations

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

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

  • Lesson 1: Introduction to Tabular Data Models
  • Lesson 2: Creating a Tabular Data Model
  • Lesson 3: Using 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.
 

  • Lesson 1: DAX Fundamentals
  • Lesson 2: Using 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.

 
  • Lesson 1: Overview of Data Mining
  • Lesson 2: Creating a Custom Data Mining Solution
  • Lesson 3: Validating a Data Mining Model
  • Lesson 4: Connecting to and Consuming a Data-Mining Model
  • Lesson 5:Using the Data Mining add-in for Excel