Developing SQL Data Models - 20768 Course Outline
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.
*** NOTE: This class also available in On-Demand, eLearning Format ***
Too busy at work to miss 5 days out of the office to take this class? Consider the MOC On-Demand version of this course. Watch videos developed by Microsoft, take the same hands-on labs, access an online expert 24/7, and study at on your own time, at your own pace. For details on this alternative format, go to
MOC On-Demand 20768: Developing SQL Data Models 2016
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.
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, 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
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 Warehouse
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 database
Module 3: Working with Cubes and Dimensions
This module describes how to implement dimensions in a cube.
Define attribute hierarchies
Sorting and grouping attributes
Lab : Working with Cubes and Dimensions
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 Groups
Module 5: Introduction to MDX
This module describes the MDX syntax and how to use MDX.
Adding calculations to a cube
Using MDX to query a cube
Lab : Using MDX
Module 6: Customizing Cube Functionality
This module describes how to customize a cube.
Implementing key performance indicators
Lab : Customizing a Cube
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 model
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.
Using DAX to create calculated columns and measures in a tabular data model
Lab : Creating Calculated Columns and Measures by using DAX
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 Mining
View outline in Word