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: 10.01.2024 - 11.04.2024
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 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.
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
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
9 Industry, Innovation and Infrastructure
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Spring III - IV
2023-2024 Spring III - IVEnrollment :
Registration for Courses: 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 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.