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LinkedIn Learning

Implementing a Data Warehouse SQL Server 2019

via LinkedIn Learning

Overview

Create a long-term data storage solution using SQL Server 2019 and Azure SQL Data Warehouse.

Dimensional models like data warehouses can provide a more accessible and consistent form of data storage than relational databases. You can consolidate data from multiple sources into a single repository for business intelligence, analysis, and reporting. This course explains how to create a long-term data storage solution using local SQL Server instances and Azure SQL Data Warehouse. Instructor Adam Wilbert shows how to build a data warehouse from the ground up, starting with the tables and views; establish control flow; enforce data quality; and use your data in services such as SQL Server Reporting Services and Power BI. By the end of the course, you will be able to implement a robust, custom solution to serve all your organization’s business intelligence, reporting, and analysis needs.

Syllabus

Introduction
  • Store information in a data warehouse
  • What you should know
  • Set up the example databases
1. Data Warehouse Foundations
  • Data warehouse core concepts
  • Transactional databases vs. data warehouses
  • Dimensions and facts
  • Star and snowflake schemas
  • Hardware and infrastructure
2. Create a Data Warehouse
  • Create a data warehouse in SQL Server
  • Design dimension tables
  • Design fact tables
  • Create an indexed view
3. Columnstore Indexes
  • Advantages of columnstore indexes
  • Memory-optimized columnstore table
  • Rebuild columnstore indexes
4. Implement an Azure SQL Data Warehouse
  • Hosting a data warehouse in the cloud
  • Create an Azure SQL Data Warehouse project
  • Develop tables in Azure SQL Data Warehouse
  • The Data Warehouse Migration Utility
  • Migrate a data warehouse to Azure
  • Pause and remove an Azure data warehouse
5. Extract, Transform, and Load (ETL)
  • What is ETL and SQL Server Integration Services (SSIS)?
  • Understand data flow
  • Establish control flow
6. Enforce Data Quality
  • SQL Server Data Quality Services (DQS)
  • Cleanse data with DQS
  • Create a custom knowledge base
7. Master Data Services
  • Introduction to Master Data Services (MDS)
  • Install MDS and IIS
  • Configure Master Data Services
  • Deploy a sample MDS model
  • Install the MDS Excel add-in
  • Update master data in Excel
8. Consume Data from the Warehouse
  • Business intelligence applications
Conclusion
  • Next steps

Taught by

Adam Wilbert

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