English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 07m | 290 MB
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.
Topics include:
- Transactional databases vs. data warehouses
- Star and snowflake schemas
- Creating a data warehouse
- Designing tables and views
- Rebuilding columnstore indexes
- Creating an Azure SQL Data Warehouse
- Establishing control flow beyond ETL
- Enforcing data quality
- Configuring Master Data Services
- Consuming data from the warehouse in BI services
Table of Contents
1 Store information in a data warehouse
2 What you should know
3 Set up the example databases
4 Data warehouse core concepts
5 Transactional databases vs. data warehouses
6 Dimensions and facts
7 Star and snowflake schemas
8 Hardware and infrastructure
9 Create a data warehouse in SQL Server
10 Design dimension tables
11 Design fact tables
12 Create an indexed view
13 Advantages of columnstore indexes
14 Memory-optimized columnstore table
15 Rebuild columnstore indexes
16 Hosting a data warehouse in the cloud
17 Create an Azure SQL Data Warehouse project
18 Develop tables in Azure SQL Data Warehouse
19 The Data Warehouse Migration Utility
20 Migrate a data warehouse to Azure
21 Pause and remove an Azure data warehouse
22 What is ETL and SQL Server Integration Services (SSIS)
23 Understand data flow
24 Establish control flow
25 SQL Server Data Quality Services (DQS)
26 Cleanse data with DQS
27 Create a custom knowledge base
28 Introduction to Master Data Services (MDS)
29 Install MDS and IIS
30 Configure Master Data Services
31 Deploy a sample MDS model
32 Install the MDS Excel add-in
33 Update master data in Excel
34 Business intelligence applications
35 Next steps
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