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Presentation
Presentation
The discipline Data Warehouse Techniques has as its fundamental objective present to the student the importance of a Data Warehouse architecture and its impact on data solutions in the business world. This curricular unit will introduce students to the motivations behind the genesis of the Data Warehouse, its main features in data storage and modeling, as well as the latest solutions such as Data Lake and Data Lakehouse applied by large technology companies. In the practical component, the student will have the opportunity to apply the concepts taught in the theoretical component, in a technology that is highly valued in the current job market.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Master Degree | Semestral | 7
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Year | Nature | Language
Year | Nature | Language
1 | Mandatory | Português
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Code
Code
ULHT457-13318
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
Theoretical classes Trends in Data and Analytics Data Warehouse Architecture Multidimensional Modeling Data Warehouse Techniques Transformation & Exploration Practical classes Introduction to Databricks and Apache Spark Data Ingestion and Staging in Databricks Transforming Data and Building a Star Schema Advanced ETL and Data Loading into Data Warehouses Querying and Visualizing Data in Databricks
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Objectives
Objectives
When concluding this curricular unit the student should: Understand the need for a Data Warehouse in a business context and describe its relationship with existing data applications (business intelligence, data analytics) Know the methodologies for implementing a Data Warehouse and know which are the best techniques to use in relation to specific business processes Describe the difference between a Data Warehouse architecture and other emerging architectures such as Data Lake, and decide which type of architecture to apply upon the business needs Get to know in practice a cloud-based platform for data engineering and to structure a data model (Databricks)
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Teaching methodologies and assessment
Teaching methodologies and assessment
The course is split into theoretical and practical classes. Students are evaluated on: Project (50%) to build a relational model from a set of data and a report presentation; Final exam (50%) with multiple choice questions and development questions. To be approved, the project grade must be higher than 9.5 and the final test grade must be higher than 9.5.
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References
References
Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (Third Edition). Indianapolis, IN: John Wiley & Sons, Inc.
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Office Hours
Office Hours
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Mobility
Mobility
No
