Data-intensive Systems DAT500
The course provides a strong basis in administrative, programing, and algorithm design aspects of data-intensive systems.
Course description for study year 2022-2023. Please note that changes may occur.
DAT500
1
10
Spring
1
Spring
English
The emergence of Big Data and Data-intensive Systems as specialized fields in computing has been motivating development of new techniques and technologies needed to extract knowledge from large datasets. Since Hadoop was conceived in 2005, popular interest in data-intensive systems began to grow. It resulted - over time - in a collection of technologies, methodologies, and practices to cover the complete data lifecycle.
This course is a first step to a variety of roles related to data-intensive systems. The core tasks in these roles that we will address are: roles in a data team, system administration (setup, containerization, test and benchmark a cluster), low-level algorithm design and implementation (direct implementation of MapReduce jobs), high-level algorithm design and implementation (utilizing one of data processing frameworks e.g. SparkSQL, MLlib), data modelling and algorithm optimisation, reliable infrastructure design for data collection and processing, advocating technology application both in technical and non-technical setting, providing introductory training to coworkers.
Knowledge:
- Characterize Hadoop job tracker, task tracker, scheduling issues, communications, and resource management
- Describe elements of Hadoop ecosystem and identify their applicability
- Describe and compare RDBMS, NOSQL databases, data warehouse, unstructured big data, and keyed files, and show how to apply them to typical data processing problems
- Understand areas of application for a variety of Cloud Computing architectures and technologies
Skills:
- Assume various roles in a data team;
- Design, construct, test, and benchmark a small data processing setup (based on Hadoop, Spark, OpenStack)
- Analyze real-life problems and propose suitable solutions
- Construct and optimize programs based directly on MapReduce paradigm for typical problems
- Construct and optimize programs based on high-level tools (for MapReduce paradigm) for typical problems
General qualifications:
- Evaluate, communicate and defend a data-intensive solution w.r.t. relevant criteria.
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Project work with oral presentation | 1/1 | Letter grades | All |
Project with oral presentation. Project is completed in groups.Both parts must be done before final grade is given. Each group member can receive a different grade based on their performance in group during the oral presentation.If a student fails the project, she/he has to take this part next time the subject is lectured.
Students start with 4 mandatory assignments that contain program and system administration. First 3 obligatory assignments are to be completed individually. All mandatory assignments must be passed within deadline so that student has the right to start with the project. The obligatory assignments give access to the project only in the current semester.
Completion of mandatory lab assignments is to be made at the times and in the groups that are assigned and published. Absence due to illness or for other reasons must be communicated as soon as possible to the laboratory personnel. One cannot expect that provisions for completion of the lab assignments at other times are made unless prior arrangements with the laboratory personnel have been agreed upon.