Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


After this course, the student is able to

  • understand big data and platforms w.r.t. services, stakeholders, interactions and state-of-the-art technologies
  • understand key interactions and performance design patterns in big data platforms
  • produce designs of big data platforms with key services like data stores, data ingestion, batch and stream processing
  • demonstrate design and implementation of big data ingestion, batch processing, streaming processing and data governance processes.
  • assess performance and reliability issues in operating big data platforms
  • deliver real-world prototypes of big data platforms with real datasets and technologies in a large-scale systems.

Credits: 5

Schedule: 12.01.2022 - 06.04.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Linh Truong

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English


  • valid for whole curriculum period:

    The course will provide  knowledge covering main aspects of big data platforms, including data platform services and ecosystems,  architectures and designs for big data, core services in big data stores, big data ingestion techniques, big data processing models (batch and streaming), and big data governance. Common aspects like users, developers and providers interactions, reliability, performance and elasticity for big data plaforms will be studied and implemented. Both design, development and operations of big data platforms are covered. 

Assessment Methods and Criteria
  • valid for whole curriculum period:

    Assigments and exams (based on Q/A for assignments). Each assignment will include theoretical concepts, big datasets, component designs, software implementation and testing, and extensibility/integration discussions. 

  • valid for whole curriculum period:

    Lectures: 10 (2), Teaching in small groups: 7 (1), Independent work, including self-study and assignments: 88

    Note the workload ratios:

    MethodTeaching hoursIndepdent workTotal workloadLecture202040Exercise707Asssignments 8888Total  


Study Material
  • valid for whole curriculum period:

    Lecture slides, tutorials, open sources, and  assignments

Substitutes for Courses


Further Information
  • valid for whole curriculum period:

    Teaching will be carried out from W37-W49. However, some weeks are used as back up dates.

    Teaching Period:

    2020-2021 Spring III-IV

    2021-2022 Spring III-IV

    Course Homepage:

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