Data Warehousing with Azure: Architecture & Modeling Techniques from Skillsoft

Online, Self-Paced

Explore the fundamentals of data warehousing and the essential architectures and components being implemented to manage data.

Learning Objectives

Data Warehousing with Azure: Architecture & Modeling Techniques

identify the essential characteristics of data warehouse and compare data warehouse with operational databases
list the various possible data warehousing architectures that can be implemented
recall the various essential data warehousing alternatives that are available today apart from Azure
depict the evolution of data warehouse and illustrate the first generation and second generation data warehouse
illustrate the various types of data warehouse that are being implemented by various businesses
list the critical features of the federated data warehouse, compare federated data warehouse with the centralized approach, and list advantages and disadvantages of this approach and implementation scenarios
identify the critical advantages and disadvantages associated with the star schema and data mart approaches of implementing data warehouse
define the essential lifecycle phases of data warehousing implementation and data movement
illustrate the essential concepts of metadata, types of metadata, and its roles in data warehouse implementation
compare the essential approaches of processing, integrating, and managing structured and unstructured data
compare the essential approaches of processing, integrating, and managing structured and unstructured data with real time examples
specify the critical analytical and reporting mechanisms that are implemented in data warehouse
define the benefits of implementing cloud data warehouse and compare cloud data warehouse with on-prem implementation
describe the benefits and how to implement Hybrid architectures using on-premise capabilities
define the critical features, characteristics, and advantages of implementing Federated and Hybrid data warehouse