Enrolment options

CS-E4640 - Big Data Platforms D, Lecture, 8.1.2025-3.4.2025

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.

LEARNING OUTCOMES

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: 08.01.2025 - 03.04.2025

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

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • 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 stakeholders (users, tenants, developers and providers) interactions, reliability, data quality, 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. 

Workload

  • valid for whole curriculum period:

    Lectures, teaching in small groups, independent work including self-study and assignments.

     

DETAILS

Study Material

  • valid for whole curriculum period:

    Lecture slides, tutorials, open sources, and assignments.

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    9 Industry, Innovation and Infrastructure

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Spring III - IV
    2025-2026 Spring III - IV

    Registration:

    Registration will be manually approved, based on the prerequisites that the students provide. Students who are not able to present the required completed courses for prerequisites must be interviewed or must perform pre-assignments for considering to be in the course. Maximum number of registered students for credits will be limited to 60-80. The course allows students who want to audit the course. However, since the course aims at making real-world prototypes in big data platforms, such students should not register the course for credits. Instead, the student just follow lectures and tutorials without registration.

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof

Guests cannot access this workspace. Please log in.