Module 1: Introduction to Data Warehousing
This module provides an introduction to the key components of a data warehousing solution and the high-level considerations you must take into account when you embark on a data warehousing project.
Lessons
- Overview of Data Warehousing
- Considerations for a Data Warehouse Solution
Lab:
- Exploring a Data Warehousing Solution
- Exploring Data Sources
- Exploring and ETL Process
- Exploring a Data Warehouse
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 project
Module 2: Planning Data Warehouse Infrastructure
This module discusses considerations for selecting hardware and distributing SQL Server facilities across servers.
Lessons
- Considerations for Data Warehouse Infrastructure
- Planning Data Warehouse
- Hardware
Lab:
- Planning Data Warehouse Infrastructure
- Planning Data Warehouse Hardware
After completing this module, you will be able to:
- Describe key considerations for BI infrastructure.
- Plan data warehouse infrastructure.
Module 3: Designing and Implementing a Data Warehouse
This module describes the key considerations for the logical design of a data warehouse, and then discusses best practices for its physical implementation.
Lessons
- Data Warehouse Design
- Overview Designing Dimension Tables
- Designing Fact Tables
- Physical Design for a Data Warehouse
Lab:
- Implementing a Data Warehouse
- Implement a Star Schema
- Implement a Snowflake Schema
- Implement a Time Dimension
After completing this module, you will be able to:
- Describe a process for designing a dimensional model for a data warehouse
- Design dimension tables for a data warehouse
- Design fact tables for a data warehouse
- Design and implement effective physical data structures for a data warehouse
Module 4: Creating an ETL Solution with SSIS
This module discusses considerations for implementing an ETL process, and then focuses on Microsoft SQL Server Integration Services (SSIS) as a platform for building ETL solutions.
Lessons
- Introduction to ETL with SSIS
- Exploring Data Sources
- Implementing Data Flow
Lab:
- Implementing Data Flow in an SSIS Package
- Exploring Data Sources
- Transferring Data by Using a Data Flow Task
- Using Transformations in a Data Flow
After completing this module, you will be able to:
- Describe the key features of SSIS.
- Explore source data for an ETL solution.
- Implement a data flow by using SSIS
Module 5: Implementing Control Flow in an SSIS Package
This module describes how to implement ETL solutions that combine multiple tasks and workflow logic.
Lessons
- Introduction to Control Flow
- Creating Dynamic Packages Using Containers
- Managing Consistency
Lab:
- Implementing Control Flow in an SSIS Package
- Using Tasks and Precedence in a Control Flow
- Using Variables and Parameters
- Using Containers
Lab:
- Using Transactions and Checkpoints
- Using Transactions
- Using Checkpoints
After completing this module, you will be able to:
- Implement control flow with tasks and precedence constraints
- Create dynamic packages that include variables and parameters
- Use containers in a package control flow
- Enforce consistency with transactions and checkpoints
Module 6: Debugging and Troubleshooting SSIS Packages
This module describes how you can debug packages to find the cause of errors that occur during execution. It then discusses the logging functionality built into SSIS that you can use to log events for troubleshooting purposes. Finally, the module describes common approaches for handling errors in control flow and data flow.
Lessons
- Debugging an SSIS Package
- Logging SSIS Package Events
- Handling Errors in an SSIS Package
Lab:
- Debugging and Troubleshooting an SSIS Package
- Debugging an SSIS Package
- Logging SSIS Package Execution
- Implementing an Event Handler
- Handling Errors in a Data Flow
After completing this module, you will be able to:
- Debug an SSIS package
- Implement logging for an SSIS package
- Handle errors in an SSIS package
Module 7: Implementing a Data Extraction Solution
This module describes the techniques you can use to implement an incremental data warehouse refresh process.
Lessons
- Planning Data Extraction
- Extracting Modified Data
Lab:
- Extracting Modified Data
- Using a Date time Column
- Using Change Data Capture
- Using the CDC Control Task
- Using Change Tracking
After completing this module, you will be able to:
- Plan data extraction
- Extract modified data
Module 8: Loading Data into a Data Warehouse
This module describes the techniques you can use to implement data warehouse load process.
Lessons
- Planning Data Loads
- Using SSIS for Incremental Loads
- Using Transact-SQL Loading Techniques
Lab:
- Loading a Data Warehouse
- Loading Data from CDC Output Tables
- Using a Lookup Transformation to Insert or Update Dimension Data
- Implementing a Slowly Changing Dimension
- Using the MERGE Statement
After completing this module, you will be able to:
- Describe the considerations for planning data loads
- Use SQL Server Integration Services (SSIS) to load new and modified data into a data warehouse
- Use Transact-SQL techniques to load data into a data warehouse
Module 9: Enforcing Data Quality
This module introduces Microsoft SQL Server Data Quality Services (DQS), and describes how you can use it to cleanse and duplicate data.
Lessons
- Introduction to Data Quality
- Using Data Quality Services to Cleanse Data
- Using Data Quality Services to Cleanse Data
Lab:
- Cleansing Data
- Creating a DQS Knowledge Base
- Using a DQS Project to Cleanse Data
- Using DQS in an SSIS Package
After completing this module, you will be able to:
- Describe how Data Quality Services can help you manage data quality
- Use Data Quality Services to cleanse your data
- Use Data Quality Services to match data
Module 10: Master Data Services
Master Data Services provides a way for organizations to standardize data and improve the quality, consistency, and reliability of the data that guides key business decisions. This module introduces Master Data Services and explains the benefits of using it.
Lessons
- Introduction to Master Data Services
- Implementing a Master Data Services Model
- Managing Master Data
- Creating a Master Data Hub
Lab:
- Implementing Master Data Services
- Creating a Master Data Services Model
- Using the Master Data Services
- Add-in for Excel Enforcing Business Rules
- Loading Data Into a Model
- Consuming Master Data Services Data
After completing this module, you will be able to:
- Describe key Master Data Services concepts
- Implement a Master Data Services model
- Use Master Data Services tools to manage master data
- Use Master Data Services tools to create a master data hub
Module 11: Extending SQL Server Integration Services
This module describes the techniques you can use to extend SSIS. The module is not designed to be a comprehensive guide to developing custom SSIS solutions, but to provide an awareness of the fundamental steps required to use custom components and scripts in an ETL process that is based on SSIS.
Lessons
- Using Scripts in SSIS
- Using Custom Components in SSIS
Lab:
- Using Custom Scripts
- Using a Script Task
After completing this module, you will be able to:
- Include custom scripts in an SSIS package
- Describe how custom components can be used to extend SSIS
Module 12: Deploying and Con
