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DATA WAREHOUSING

CODE 101798
ACADEMIC YEAR 2022/2023
CREDITS
  • 9 cfu during the 1st year of 10852 COMPUTER SCIENCE (LM-18) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR INF/01
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 1° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    The course introduces data engineering skills that are at the basis of structured data analysis and Business Intelligence. Students will llearn the basics of structured data modeling for analysis, develop an in-depth understanding of data warehouse design and data manipulation and gain practical experience in formulating OLAP (online analytical processing) queries in SQL. Large scale data analysis frameworks will also be introduced. In the practical activities, students will work with large data sets in a data warehouse environment to design and populate a data warehouse, query it and create dashboards, using BI and ETL tools as well as OLAP servers. In the final project, such skills will be applied to build a small, basic data warehouse, populate it with data, and create dashboards and other visualizations to analyze and communicate the data to a broad audience.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    Learning the theoretical, methodological, and technological fundamentals of data management and analysis in decision support systems, with a specific reference to data warehousing architectural and design issues, as well as key elements of data integration and governance, data quality and cleaning, ExtractionTransformation-Loading processes, conceptual, logical, and physical design of data warehouses, storage architectures and scalable parallel processing, use of data warehouses for business reporting and online analytical processing.

    AIMS AND LEARNING OUTCOMES

    DESCRIBE the principles for data analysis and large-scale data analysis

    UNDERSTAND the differences between data management (OLTP) systems and data analysis (OLAP) systems

    UNDERSTAND the differences between design issues and methodologies for databases and for datawarehouses

    UNDERSTAND the main issues in data quality, data integration, and data governance

    UNDERSTAND the main issues in data warehouse design, with specific reference to conceptual design, ROLAP logical design, view selection, physical design and ETL design

    UNDERSTAND the main issues in large scale data analysis 

    SELECT the most adequate systems and languages for a given analysis context

    USE some of the presented systems for data exploration, data reconciliation, data warehouse storage, data reporting and OLAP querying

    USE some of the presented systems for construncting a datawarehouse from a given operational dataset and for performing non-trivial analyses on it

    SOLVE exercizes related to the design of data warehouses and OLAP queries

    PREREQUISITES

    Fundamentals of database models, languages, and systems.

    • Entity-relationship (conceptual) model
    • Relational (logical) model
    • Schema normalization
    • Relational algebra & SQL
    • Indexes
    • Transactions

    TEACHING METHODS

    Class, project, and outside preparation.

    SYLLABUS/CONTENT

    The course will present the main architectural and design issues related to data management and analysis in data support systems (data warehousing), comparing them with traditional transactional systems.

    • Introduction. IT technologies to support decisions. Differences between OLAP and OLTP. Data warehousing and Data mining. Business Intelligence.
    • Data integration and data quality.
    • Data models for data warehouses. Conceptual data model. Dimensions, measures and hierarchies. Multidimensional data model. ROLPAP models: star and snowflake schemas.
    • Back-end.Architectures. Storage structures and indexes in OLAP. Materialized views. Optimization of OLAP queries.
    • Data warehouse design. Conceptual, logical and physical design.
    • ETL functionalities and approaches.
    • Front-end. OLAP queries and reporting. SQL OLAP extensions. 
    • Large scale data analysis: Hive and SparkSQL

    RECOMMENDED READING/BIBLIOGRAPHY

    • ​M. Golfarelli, S. Rizzi. Data Warehouse Design. Mc-Graw Hill 2009. 
    • R. Kimball, M. Ross. The Data Warehouse Toolkit. Wiley, 2013.
    • C. Jensen, T. Bach Pedersen, C. Thomsen. Multidimensional Databases and Data Warehousing. Morgan&Claypool, 2010.
    • A.Vaisman, E. Zimányi. Data Warehouse Systems: Design and Implementation. Springer, 2014

    TEACHERS AND EXAM BOARD

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    Written examination, oral examination (including project discussion).

    ASSESSMENT METHODS

    Details on how to prepare for the examination and the required degree of knowledge for each topic will be provided during the lessons.

    During the semester,  some assignments (groupwork) as well as a project will be proposed. The project is mandatory.

    The written exam consists of a set of questions and exercizes on basic topics of the course; the goal of this test is to verify the understanding of the main issues addressed during the lessons.

    The oral exam consists of an in-depth discussion of the solutions developed by the student for the given project, in order to assess whether the student has reached an appropriate level of knowledge. For students that do not successfully complete the assignments the oral exam will also include theoretical questions and / or practices of the course topics.