Course Description
Explore the fundamentals of data warehousing and the approaches of implementing it.
Learning Objectives
Data Warehouse Essential: Concepts
- identify the characteristics of strategic information and the need for data warehousing to manage strategic information
- list the essential differences between OLAP and data warehousing capabilities
- specify the essential guidelines that should be followed in order to implement a successful data warehouse project on the cloud and on-premise
- compare essential on-premise and on-cloud data warehousing products and components
- identify the essential characteristics of data warehouse projects
- compare normalization and denormalization processes in data warehouse projects
- compare the contrasting features of OLTP and data warehouse from the perspective of indexes, joins, data duplication, and data aggregation
- differentiate global data warehouse from local data warehouse, and recognize critical features, capabilities, and implementation
- recall essential data warehouse terms that are frequently used when implementing data warehouse projects
- recall important data warehouse processes that are generally applied to facilitate business intelligence, including the essential ETL processes
- recall how the ER schemas are implemented in data warehouse projects
- specify how the star schemas are implemented in data warehouse projects
- describe how the snowflake schemas are implemented in data warehouse projects
- identify the critical capabilities of multi-valued dimensions and the essential comparison between weighted and impact reports
- illustrate the architectural concept of reporting and classify the various essential types of reports
- compare data warehouse, RDBM, data lake and their implementation scenarios
- compare the critical features, capabilities, and the implementation scenarios of Azure and AWS data lakes
- identify how to implement and facilitate data warehouse given a scenario