Audience
The primary audience for this course are database professionals who need to fulfil a Business Intelligence Developer role. They will need to focus on hands-on work creating BI solutions including Data Warehouse implementation, ETL, and data cleansing.
Prerequisites
In addition to their professional experience, students who attend this training should already have the following technical knowledge:
- Basic knowledge of the Microsoft Windows operating system and its core functionality.
- Working knowledge of relational databases.
- Some experience with database design.
Duration
5 days. Hands on.
Course Objectives
This five-day instructor-led course provides students with the knowledge and skills to provision a Microsoft SQL Server database. The course covers SQL Server provision both on-premise and in Azure, and covers installing from new and migrating from an existing install.
After completing this course, students will be able to:
- Describe the key elements of a data warehousing solution
- Describe the main hardware considerations for building a data warehouse
- Implement a logical design for a data warehouse
- Implement a physical design for a data warehouse
- Create columnstore indexes
- Implementing an Azure SQL Data Warehouse
- Describe the key features of SSIS
- Implement a data flow by using SSIS
- Implement control flow by using tasks and precedence constraints
- Create dynamic packages that include variables and parameters
- Debug SSIS packages
- Describe the considerations for implementing an ETL solution
- Implement Data Quality Services
- Implement a Master Data Services model
- Describe how you can use custom components to extend SSIS
- Deploy SSIS projects
- Describe BI and common BI scenarios
Course Content
Module 1: Introduction to Data Warehousing
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
Considerations for data warehouse infrastructure
Planning data warehouse hardware.
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
Data warehouse design overview
Designing dimension tables
Designing fact tables
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
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
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
Copying data with the Azure data factory
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
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
Introduction to Control Flow
Creating Dynamic Packages
Using Containers
Managing consistency
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
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 a Data Extraction Solution
Introduction to Incremental ETL
Extracting Modified Data
Loading modified data
Temporal Tables
Lab : Extracting Modified Data
Lab : Loading a data warehouse
After completing this module, you will be able to:
• Describe incremental ETL
• Extract modified data
• Load modified data.
• Describe temporal tables
Module 10: Enforcing Data Quality
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
Introduction to Master Data Services
Implementing a Master Data Services Model
Hierarchies and collections
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)
Using scripting in SSIS
Using custom components in SSIS
Lab : Using scripts
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
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
Introduction to Business Intelligence
An Introduction to Data Analysis
Introduction to reporting
Analyzing Data with Azure SQL Data Warehouse
Lab : Using a data warehouse
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