Data Integration and Large-Scale Analysis WS2020/21
(VU, 706.520 Data Integration and Large-Scale Analysis)

DIA is a 5 ECTS bachelor and master course, applicable to the bachelor programs computer science or software engineering and management, as well as the master catalog 'Data Science'. This course covers major data integration architectures, key techniques for data integration and cleaning, as well as methods for large-scale, i.e., distributed, data storage and analysis.


In detail, the course covers the following topics, which also reflects the course calendar. All slides will be made available prior to the individual lectures.

A: Data Integration and Preparation

  • 01 Introduction and Overview [Oct 09, pdf, pptx]
  • 02 Data Warehousing, ETL, and SQL/OLAP [Oct 16, pdf, pptx]
  • 03 Message-oriented Middleware, EAI, and Replication [Oct 23, pdf, pptx]
  • 04 Schema Matching and Mapping [Oct 30, pdf, pptx]
  • 05 Entity Linking and Deduplication [Nov 06, pdf, pptx]
  • 06 Data Cleaning and Data Fusion [Nov 13, pdf, pptx]
  • 07 Data Provenance and Blockchain [Nov 20, pdf, pptx]

B: Large-Scale Data Management and Analysis

  • 08 Cloud Computing Fundamentals [Nov 27, pdf, pptx]
  • 09 Cloud Resource Management and Scheduling [Dec 04, pdf, pptx]
  • 10 Distributed Data Storage [Dec 11, pdf, pptx]
  • 11 Distributed, Data-Parallel Computation [Jan 08, pdf, pptx]
  • 12 Distributed Stream Processing [Jan 15, pdf, pptx]
  • 13 Distributed Machine Learning Systems [Jan 22, pdf, pptx]


The lectures are accompanied by mandatory programming projects (to the extend of 2 ECTS, i.e, roughly 50 working hours), preferrably in Apache SystemDS (an open source ML system for the end-to-end data science lifecycle) instead.

A list of project proposals (and some details on the alternative exercise) can be found at the end of Lecture 02.


  • Lecturer: Univ.-Prof. Dr.-Ing. Matthias Boehm, ISDS
  • Teaching Assistant: M.Sc. Shafaq Siddiqi, ISDS
  • Final written/oral exams: Feb 08 (DIA), Apr 29 (DIA), oral exams
  • Grading: 40% project, 60% final exam