Description
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
