Data Warehouses

Code:

EIC0046

Acronym:

ADAD

Keywords
Classification	Keyword
OFICIAL	Information Systems

Instance: 2020/2021 - 1S

Active?	Yes
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Informatics and Computing Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIEIC	20	Syllabus since 2009/2010	5	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

The students should be able to design, build and explore data warehouses.

Learning outcomes and competences

(1) Plan and manage the lifecycle of a data warehouse project; (2) Identify the requirements and the data sources; (3) Design a suitable dimensional model; (4) Design and implement a data extraction, transformation and loading process ; (5) Specify and implement applications and models to access and visualize the data warehouse information; (6) Optimize the data warehouse through the creation of aggregations and indexes; (7) Build and explore multidimensional implementations; (8) Query multidimensional implementations; (9) Define the required  metadata for audit purposes.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Relational databases, normalization, SQL.

Program

• Key Performance Indicators


• Dimensions and hierarchies


• Data Warehouse Conceptual Modeling: the MultiDim model


• The Enterprise Data Warehouse: implementation in a relational database


• Kimball Data Marts: implementation in a relational database


• Extraction, Transformation & Loading: experiments with an ETL tool


• The multidimensional model


• Experiments with an OLAP server


• The MDX language


• How to design dashboards: experiments with a dashboard tool


• Fundamentals on agile development of Data Warehousing projects


• Using data analytic tools on OLAP cubes


• Metadata requirements

 

Mandatory literature

Vaisman Alejandro; Data warehouse systems. ISBN: 978-3-642-54655-6

Complementary Bibliography

Harold Kerzner; Project Management Metrics, KPIs, and Dashboards: A Guide to Measuring and Monitoring Project Performance, Wiley, 2017. ISBN: 978-1119427285
Inmon, W. H.; Building the data warehouse. ISBN: 0-471-08130-2
Kimball Ralph 070; The data warehouse lifecycle toolkit. ISBN: 9781118075043
Rick Sherman; Business Intelligence Guidebook: From Data Integration to Analytics, Morgag Kaufmann, 2014. ISBN: 978-0124114616
Lawrence Corr, Jim Stagnitto; Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema, DecisionOne Press, 2011. ISBN: 978-0956817204

Teaching methods and learning activities

The lectures follow a mixed expositive and problem solving method, where the discussion of new topics and the description of techniques is intertwined with the resolution of exercises and lab work. Due to the applied goals of the course, the assessment method is 50% based on a medium sized lab assignment, executed by two person groups, preferably with real data, in which the several steps of a data warehouse project are followed. In the end, besides the project report, an oral presentation and discussion takes place, as happens in real world projects. The remaining 50% correspond to an individual written exam, where the students have the opportunity to demonstrate their mastering of the issues not covered by their lab assingment.

Software

Pentaho OLAP server
mySQL
Power BI
Kettle

keywords

Physical sciences > Computer science > Database management

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	50,00
Participação presencial	0,00
Trabalho laboratorial	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	80,00
Frequência das aulas	42,00
Trabalho laboratorial	40,00
Total:	162,00

Eligibility for exams

Distributed evaluation requires a minimum of 7,5/20.

Calculation formula of final grade

Final Exam: 50%
Lab assignment: 50%

Final exam must score more than 7,5.

Examinations or Special Assignments

Medium size lab assignment.

Special assessment (TE, DA, ...)

Students with special status must deliver and present the lab assignment and answer the examination at the same dates as the other students.

Classification improvement

The final exam can be improved with an Exam for Classification Improvement.
The lab assignment can be improved by a new assignment to be defined by the teacher.

Observations

Pre-requisites: relational model, SQL, normalization theory.