Implementing a SQL Data Warehouse (20767)

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, students will be able to:

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

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, students 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

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, students will be able to:

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

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, students will be able to:

• Create Columnstore indexes
• Work with Columnstore Indexes

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, students 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

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, students will be able to:

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

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, students will be able to:

• Describe control flow
• Create dynamic packages
• Use containers

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, students will be able to:

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

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, students will be able to:

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

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, students 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

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, students 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

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, students will be able to:

• Use custom components in SSIS
• Use scripting in SSIS

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, students will be able to:

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

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, students 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