Data Warehouse Essential: Architecure Frameworks and Implementation

Data Warehouse Essential: Architecure Frameworks and Implementation

• recall essential architectural components of data warehouse along with the design considerations to successfully implement the right data warehousing solution
• specify essential data warehouse architectural styles and frameworks, and compare Kimball and Inmon
• identify the logical and physical data warehousing architectural models, with the emphasis on schema and objects
• illustrate implementation scenarios of the logical and physical data warehousing models
• list dimensional modeling principles and the differences between it and the ER model
• specify how data warehousing can be used to facilitate information realization
• illustrate the processes of extracting, loading, and transforming data in a data warehousing environment
• recall data warehousing and ETL tools and specify how they are implemented real-time
• recognize essential tools, components, features and how they are used to implement ETL
• demonstrate how to use Talend to implement ETL
• extract data from various sources, and transform and load the data in the intended destination

Examine architectures of data warehouse implementations, including logical and physical design. How to effectively implement and manage data warehousing projects is also covered.

Data Warehouse Essential: Concepts

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

Explore the fundamentals of data warehousing and the approaches of implementing it.