This course delves into the fundamental concepts and skills essential for designing data warehouses and creating data integration workflows. It provides hands-on experience with open source products for manipulating pivot tables and creating data integration workflows. Learners will gain insights into data warehouse maturity models, architectures, multidimensional models, and management practices.
Through this course, participants will be able to evaluate organizations for data warehouse maturity, create data warehouse designs, and reflect on alternative design methodologies and design goals. They will also learn to create data integration workflows using prominent open source software and perform operations on pivot tables to satisfy typical business analysis requests.
The course is ideal for business or information technology professionals aiming to become proficient data warehouse designers or administrators. By the end of the course, learners will be well-prepared with the necessary design experience, software background, and organizational context for success in data warehouse development projects.
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This course comprises modules covering Data Warehouse Concepts and Architectures, Multidimensional Data Representation and Manipulation, Data Warehouse Design Practices and Methodologies, Data Integration Concepts, Processes, and Techniques, and Architectures, Features, and Details of Data Integration Tools.
This module introduces participants to data warehouse architectures, maturity models, and employment opportunities. It covers essential concepts such as motivation, characteristics, and learning effects for data warehouse development. Learners will also gain insights into the overview of database software requirements and installation, as well as Pentaho Data Integration installation.
In this module, participants will delve into data cube representation, operators, and Microsoft MDX statements. They will also explore the WebPivotTable software and tutorial, gaining hands-on experience in multidimensional data representation and manipulation. The module concludes with an assignment and solution for the WebPivotTable.
Participants will learn about relational database concepts for multidimensional data, table design patterns, and schema integration. This module provides insights into summarizability patterns for dimension tables and dimension-fact relationships, along with data warehouse design methodologies. It culminates with a mini case study and solution, as well as a self-evaluation for the assignment.
This module covers data integration processes, change data concepts, data cleaning tasks, and pattern matching with regular expressions. Participants will also explore SQL for data integration and gain insights into quasi identifiers and distance functions for entity matching. The module concludes with an assignment, self-evaluation, and reflective quiz.
In the final module, learners will delve into architectures and marketplace, common features of data integration tools, and specific tools such as Talend Open Studio and Pentaho Data Integration. They will engage in a guided tutorial for Pentaho Data Integration and complete the module assignment, along with a self-evaluation and reflective quiz.
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