This course describes how to implement a data warehouse platform to support a BI solution. Students will learn how to create a data warehouse with Microsoft SQL Server 2019 or earlier, implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.
Implementing Data Warehouses with Integration Services
Who should attend this course?
This course is intended for database professionals who need to create and support a data warehousing solution
This course does not assume any prior knowledge with SQL Server Integration Services or building a relational data warehouse.
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.
- Overview of Data Warehousing
- Considerations for a Data Warehouse Solution
- LAB: Inspecting an SSIS Solution
Planning Data Warehouse Infrastructure
This module discusses considerations for selecting hardware and distributing SQL Server facilities across servers.
- Considerations for Data Warehouse Infrastructure
- Planning Data Warehouse Hardware
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.
- Data Warehouse Design Overview
- Designing Dimension Tables
- Designing Fact Tables
- Physical Design for a Data Warehouse
- LAB: Implementing dimension and fact tables in SQL Server
- Introduction to Columnstore Indexes
- Creating Columnstore Indexes
- Working with Columnstore Indexes
- LAB: creating and querying ColumnStore Indexes in SQL Server
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
Creating an ETL Solution
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.
- Introduction to ETL with SSIS
- Exploring Data Sources
- Implementing Data Flow
- LAB: Developing an SSIS data flow in Visual Studio
Implementing Control Flow in an SSIS Package
This module describes how to implement ETL solutions that combine multiple tasks and workflow logic.
- Introduction to Control Flow
- Creating Dynamic Packages
- Using Containers
- Managing Consistency
- LAB: Creating an SSIS package control flow
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.
- Debugging an SSIS Package
- Logging SSIS Package Events
- Handling Errors in an SSIS Package
- LAB: Debugging, logging and event handlers in SSIS
Implementing a Data Extraction Solution
This module describes the techniques you can use to implement an incremental data warehouse refresh process.
- Planning Data Extraction
- Extracting Modified Data
- LAB: Implementing an incremental data load
Enforcing Data Quality
Ensuring the high quality of data is essential if the results of data analysis are to be trusted. SQL Server includes Data Quality Services (DQS) to provide a computer-assisted process for cleansing data values, as well as identifying and removing duplicate data entities. This process reduces the workload of the data steward to a minimum while maintaining human interaction to ensure accurate results.
- Introduction to Data Quality
- Using Data Quality Services to Cleanse Data
- Using Data Quality Services to Cleanse Data
- LAB: Data Quality Services
Master Data Services
Master Data Services provides a way for organizations to standardize 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.
- Introduction to Master Data Services
- Implementing a Master Data Services Model
- Managing Master Data
- Creating a Master Data Hub
- LAB: Working with Master Data Services
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, based on SSIS.
- Using Scripts in SSIS
- Using Custom Components in SSIS
- LAB: Using the Script task and Script component
Deploying and Configuring SSIS Packages
Microsoft SQL Server Integration Services (SSIS) provides tools that make it easy to deploy packages to another computer. The deployment tools also manage any dependencies, such as configurations and files that the package needs. In this module, you will learn how to use these tools to install packages and their dependencies on a target computer.
- Overview of SSIS Deployment
- Deploying SSIS Projects
- Planning SSIS Package Execution
- LAB: Deploying packages in project and in package deployment mode
Consuming Data in a Data Warehouse
This module introduces BI, describing the components of Microsoft SQL Server that you can use to create a BI solution, and the client tools with which users can create reports and analyze data.
- Introduction to Business Intelligence
- Enterprise Business Intelligence
- Self-Service BI and Big Data
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