|Course Title||Big Data Management|
|Units of Credit||6|
This course introduces the core concepts and technologies involved in managing Big Data. It will first introduce the characteristics of big data and big data analysis. Then, we will learn the open-source big data management framework Hadoop. We will mainly focus on the learning of Hadoop MapReduce, and HDFS, HBase, Hive, and Pig will be briefly introduced. We will also learn an open-source memory-based distributed computing framework Spark. Another major focus of this course is algorithm design on large-scale data sets based on the big data management frameworks, in a variety of domains such as link analysis, data stream mining, graph data processing, and machine learning.
Lecture Time (3 hours):
Consultation Time (1 hour):
The complete course timetable is available here .
This course aims to introduce students to the concepts behind Big Data, the core technologies used in managing large-scale data sets, and a range of technologies for developing solutions to large-scale data analytics problems.
This course is intended for students who want to understand modern large-scale data analytics systems. It covers a wide range of topics and technologies, and will prepare students to be able to build such systems as well as use them efficiently and effectively address challenges in big data management.
After completing this course, students will:
Official prerequisite of this course is COMP9024 and COMP9311. Before commencing this course, students should:
The course involves lectures and practical work. Lectures aim to summarize the concepts and present case studies. The lab exercises aim to reinforce the topics covered in lectures (without assessment), while the assignments and projects aim to do the same (but are assessed). The course will have an emphasis on problem solving for large-scale data sets.
|2*||Coding Project 1||10|
|3**||Coding Project 2||25|
|4**||Coding Project 3||20|
|5**||Coding Project 4||25|
Later Submission Penalties:
* : zero marks
** : 10% reduction of your marks for the 1st day, 30% reduction/day for the following days
The final mark is calculated by the harmonic mean:
Final Mark= 2 * (ass1 + proj1 + proj2 + proj3 + proj4) * FinalExam / (ass1 + proj1 + proj2 + proj3 + proj4 + FinalExam)
You also need to achieve at least 40 marks in the final exam to pass the course.
The order that topics are covered in this course is probably not the best order for presenting them. The material is presented in lectures in an order that ensures that you are best prepared for the assignments.
The precise schedule is subject to change as the semester progresses.
|1||Course information and introduction to big data|
|2||Hadoop MapReduce 1|
|3||Hadoop MapReduce 2 (proj1)|
|4||Hadoop MapReduce 3|
|5||Graph data processing in MapReduce (proj2)|
Spark 2 (proj3)
Data stream mining
Finding Similar Items (proj4)
NoSQL and High Level MapReduce Tools (ass1)
|12||Revision and exam preparation|
The textbooks include:
Other references include:
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Resource created Wednesday 19 July 2017, 03:43:56 AM, last modified Sunday 23 July 2017, 11:40:38 PM.