• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

ETL and Big Data Tools

2023/2024
Academic Year
ENG
Instruction in English
3
ECTS credits
Course type:
Elective course
When:
1 year, 2 module

Course Syllabus

Abstract

Large enterprises collect, store and process different types of data from a variety of sources, such as payroll systems, sales records, inventory systems and others. This information is extracted, transformed and transferred to data warehouses using ETL systems. Let's tell you what ETL is, as well as what paid and publicly available solutions for working with data are on the market. This working program of the discipline establishes the minimum requirements for the knowledge and skills of the student, as well as determines the content and types of training sessions and reporting. The program is designed for teachers leading the discipline "ETL and big data tools", teaching assistants and students of 38.04.01. Economics, studying in the educational program "Economics".
Learning Objectives

Learning Objectives

  • The objectives of the discipline "ETL and big data tools", are: • getting students knowledge about methods of extracting and transforming data; • identify the best data tools; • find solutions to combine data from multiple sources; • bringing to a unified terminology and unified metrics for working with DWH
Expected Learning Outcomes

Expected Learning Outcomes

  • Can find data in open databases
  • Can meet the requirements for written work
  • Can use different application data pro-cessing packages
  • Justifies the plan of data search for statisti-cal research of real economic situation, forms the system of initial indicators, pre-pares the data matrix according to the set analytical task, masters the skills of material structuring, checks information from differ-ent sources for methodological comparability
  • Can make cross-country comparisons.
  • Can collect, process, and aggregate statisti-cal data.
Course Contents

Course Contents

  • Topic 1. General information about ETL
  • Topic 2. ETL role in analytics
  • Topic 3. ETL or ELT
  • ETL tools
  • Topic 5. ETL for MDM
  • Topic 6. DWH
  • Topic 7. Data processing practice
Assessment Elements

Assessment Elements

  • non-blocking Intermediate attestation
  • Partially blocks (final) grade/grade calculation Final Exam
Interim Assessment

Interim Assessment

  • 2023/2024 2nd module
    0.5 * Final Exam + 0.5 * Intermediate attestation
Bibliography

Bibliography

Recommended Core Bibliography

  • Data warehouse : from architecture to implementation, Devlin, B., 1997
  • Kimball, R., & Caserta, J. (2004). The Data Warehouse ETL Toolkit : Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Indianapolis, IN: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=124355

Recommended Additional Bibliography

  • Martin Oberhofer, Eberhard Hechler, Ivan Milman, Scott Schumacher, & Dan Wolfson. (2014). Beyond Big Data : Using Social MDM to Drive Deep Customer Insight. [N.p.]: IBM Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1600785