Big Data is nowadays manifested in a very large number of environments and application fields pertaining to our education, entertainment, health, public governance, enterprising, etc. The course will endow students with the understanding of the new challenges big data introduces and the currently available solutions. These include (i) challenges pertaining to the modelling, accessing, and storing of big data, (ii) an understanding of the fundamentals of systems designed to store and access big data, and (iii) programming paradigms for efficient scalable access to big data.

3 hours contact time per week

Big Data (H)

 None

Examination 75%, Coursework 25%.

The course aims to endow students with:

An understanding of the new challenges posed by the advent for big data, as they refer to its modelling, storage, and access, paying particular emphasis on the impact of the desiderata of scalability and efficiency in big data infrastructures.

Exposure to a number of different cloud data stores and their design and implementation details, showing how they can achieve efficiency and scalability, while also addressing design trade-offs and their impacts.

Familiarity with modern programming paradigms (e.g., MapReduce, RDDs, etc.), so to enable them to design and develop programs which can execute in massively parallel infrastructures in the cloud.

The ability to discuss and appraise the internals of (NoSQL) cloud data storage systems, and the ability to enrich these systems with additional functionality.

A deep knowledge of the latest evolutions in the field of big data systems.

By the end of this course students will be able to:

1. Design, develop and evaluate programs to access big data repositories in a massively parallel manner;

2. Discuss and contrast the internals of the design and implementation of current cloud data storage and processing systems;

3. Identify and analyse issues related to the scalability and efficiency challenges of processing complex queries/algorithms against big data systems, and develop and assess ways of addressing said challenges;

4. Explain and appraise state-of-the-art research in the field of big data systems.

5. Demonstrate that they have mastered the required background knowledge to pursue graduate studies in the fields of cloud systems and big data.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment, as well as at least one of the assessed exercises of the course.