MCSA: SQL Server 2016 BI Development Boot Camp

***RETIRING ON 1/31/2021***

Course Outline

Exam 1

Module 1: Introduction to Data Warehousing

Describe data warehouse concepts and architecture considerations.

Lessons

• Overview of Data Warehousing
• Considerations for a Data Warehouse Solution

Lab: Exploring a Data Warehouse Solution

After completing this module, you will be able to:

• Describe the key elements of a data warehousing solution
• Describe the key considerations for a data warehousing solution

Module 2: Planning Data Warehouse Infrastructure

This module describes the main hardware considerations for building a data warehouse.

Lessons

• Considerations for Building a Data Warehouse
• Data Warehouse Reference Architectures and Appliances

Lab: Planning Data Warehouse Infrastructure

After completing this module, you will be able to:

• Describe the main hardware considerations for building a data warehouse
• Explain how to use reference architectures and data warehouse appliances to create a data warehouse

Module 3: Designing and Implementing a Data Warehouse

This module describes how you go about designing and implementing a schema for a data warehouse.

Lessons

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

Lab: Implementing a Data Warehouse Schema

After completing this module, you will be able to:

• Implement a logical design for a data warehouse
• Implement a physical design for a data warehouse

Module 4: Columnstore Indexes

This module introduces Columnstore Indexes.

Lessons

• Introduction to Columnstore Indexes
• Creating Columnstore Indexes
• Working with Columnstore Indexes

Lab: Using Columnstore Indexes

After completing this module, you will be able to:

• Create Columnstore indexes
• Work with Columnstore Indexes

Module 5: Implementing an Azure SQL Data Warehouse

This module describes Azure SQL Data Warehouses and how to implement them.

Lessons

• Advantages of Azure SQL Data Warehouse
• Implementing an Azure SQL Data Warehouse
• Developing an Azure SQL Data Warehouse
• Migrating to an Azure SQ Data Warehouse

Lab: Implementing an Azure SQL Data Warehouse

After completing this module, you will be able to:

• Describe the advantages of Azure SQL Data Warehouse
• Implement an Azure SQL Data Warehouse
• Describe the considerations for developing an Azure SQL Data Warehouse
• Plan for migrating to Azure SQL Data Warehouse

Module 6: Creating an ETL Solution

At the end of this module you will be able to implement data flow in a SSIS package.

Lessons

• Introduction to ETL with SSIS
• Exploring Source Data
• Implementing Data Flow

Lab: Implementing Data Flow in an SSIS Package

After completing this module, you will be able to:

• Describe ETL with SSIS
• Explore Source Data
• Implement a Data Flow

Module 7: Implementing Control Flow in an SSIS Package

This module describes implementing control flow in an SSIS package.

Lessons

• Introduction to Control Flow
• Creating Dynamic Packages
• Using Containers

Lab: Implementing Control Flow in an SSIS Package

Lab: Using Transactions and Checkpoints

After completing this module, you will be able to:

• Describe control flow
• Create dynamic packages
• Use containers

Module 8: Debugging and Troubleshooting SSIS Packages

This module describes how to debug and troubleshoot SSIS packages.

Lessons

• Debugging an SSIS Package
• Logging SSIS Package Events
• Handling Errors in an SSIS Package

Lab: Debugging and Troubleshooting an SSIS Package

After completing this module, you will be able to:

• Debug an SSIS package
• Log SSIS package events
• Handle errors in an SSIS package

Module 9: Implementing an Incremental ETL Process

This module describes how to implement an SSIS solution that supports incremental DW loads and changing data.

Lessons

• Introduction to Incremental ETL
• Extracting Modified Data
• Temporal Tables

Lab: Extracting Modified Data

Lab: Loading Incremental Changes

After completing this module, you will be able to:

• Describe incremental ETL
• Extract modified data
• Describe temporal tables

Module 10: Enforcing Data Quality

This module describes how to implement data cleansing by using Microsoft Data Quality services.

Lessons

• Introduction to Data Quality
• Using Data Quality Services to Cleanse Data
• Using Data Quality Services to Match Data

Lab: Cleansing Data

Lab: De-duplicating Data

After completing this module, you will be able to:

• Describe data quality services
• Cleanse data using data quality services
• Match data using data quality services
• De-duplicate data using data quality services

Module 11: Using Master Data Services

This module describes how to implement master data services to enforce data integrity at source.

Lessons

• Master Data Services Concepts
• Implementing a Master Data Services Model
• Managing Master Data
• Creating a Master Data Hub

Lab: Implementing Master Data Services

After completing this module, you will be able to:

• Describe the key concepts of master data services
• Implement a master data service model
• Manage master data
• Create a master data hub

Module 12: Extending SQL Server Integration Services (SSIS)

This module describes how to extend SSIS with custom scripts and components.

Lessons

• Using Custom Components in SSIS
• Using Scripting in SSIS

Lab: Using Scripts and Custom Components

After completing this module, you will be able to:

• Use custom components in SSIS
• Use scripting in SSIS

Module 13: Deploying and Configuring SSIS Packages

This module describes how to deploy and configure SSIS packages.

Lessons

• Overview of SSIS Deployment
• Deploying SSIS Projects
• Planning SSIS Package Execution

Lab: Deploying and Configuring SSIS Packages

After completing this module, you will be able to:

• Describe an SSIS deployment
• Deploy an SSIS package
• Plan SSIS package execution

Module 14: Consuming Data in a Data Warehouse

This module describes how to debug and troubleshoot SSIS packages.

Lessons

• Introduction to Business Intelligence
• Introduction to Reporting
• An Introduction to Data Analysis
• Analyzing Data with Azure SQL Data Warehouse

Lab: Using Business Intelligence Tools

After completing this module, you will be able to:

• Describe at a high level business intelligence
• Show an understanding of reporting
• Show an understanding of data analysis
• Analyze data with Azure SQL data warehouse

Exam 2

Module 1: Introduction to Business Intelligence and Data Modeling

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

Lessons

• Introduction to Business Intelligence
• The Microsoft business intelligence platform

Lab: Exploring a Data Warehouse

After 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.

Lessons

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

Lab: Creating a multidimensional database

After 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.

Lessons

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

Lab: Working with Cubes and Dimensions

After 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.

Lessons

• Working with measures
• Working with measure groups

Lab: Configuring Measures and Measure Groups

After 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.

Lessons

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

Lab: Using MDX

After 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.

Lessons

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

Lab: Customizing a Cube

After 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.

Lessons

• 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

After 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.

Lessons

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

Lab: Creating Calculated Columns and Measures by using DAX

After 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.

Lessons

• 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

After 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