FIT3182 · Big data management and processing

Overview

Data engineering is about developing the software (and hardware) infrastructure to support data science. This unit introduces software tools and techniques for data engineering, but not hardware. It will cover an introduction to big data processing, covering volume, variety, and velocity; large volume data processing using parallel technologies; variety data formats, including unstructured and semi-structured data, using NoSQL databases; and velocity data processing, covering data streaming.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. identify big data concepts and technologies;
2. design and develop parallel database processing algorithms and methods;
3. explain a variety of data formats in big data;
4. apply modern big data processing tools for various data structures;
5. solve complex big data streaming problems.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.

Student experience, not official rules. Nothing here changes what the Handbook says.