Big Data Platforms

To attend this course, you must have

• basic programming skills (Python),
• skills to work with command line tools in Linux, and
• basic knowledge of database systems (SQL).

This course focuses on big data platforms and on key algorithmic ideas and methods used to implement them.

Contents

Advanced topics in cloud computing with emphasis on scalable big data platforms employed in cloud computing. Key cloud computing technologies and their algorithmic background.

Main topics are:

• distributed computing,
• Warehouse-Scale Computers,
• fault tolerance in distributed systems,
• distributed file systems,
• distributed batch processing with the MapReduce and the Apache Spark computing frameworks,
• stream processing,
  
• distributed cloud based databases, and
  
• Data Warehouse and Lakehouse architectures.
  

After completing this course you are able to

• list many of the key technologies used in big data processing,
• select suitable methods for solving challenging big data processing tasks using cloud computing technologies, and
• compare the scalability and fault tolerance implications of using the selected methodologies.

The course material will consist of lecture materials and exercises provided by the lecturer.

As optional reading the students can read the following freely available textbook that unfortunately covers only a part of the course contents but also has extra material beyond the course scope:

• Luiz André Barroso, Urs Hölzle, Parthasarathy Ranganathan:
  The Datacenter as a Computer: Designing Warehouse-Scale Machines, Third Edition
  Synthesis Lectures on Computer Architecture
  October 2018, 189 pages,
  https://doi.org/10.2200/S00874ED3V01Y201809CAC046(Öppnas i en ny flik)

Target group

The course is suitable to those who are interested in big data platforms employed in cloud computing and have previous knowledge in programming, database systems and command line tools.

• Core course in Data Science Master's Program.
• Optional course for Computer Science Master's Program students.
• Suitable for Exchange students with basic programming (Python) and Database (SQL) prerequisite knwoledge.
  

Timing

• First or second year of Master's curriculum.
• Autumn term, period 1.

Completion methods

The course can be studied remotely as a MOOC. Home exercises and lecture quizzes are required to pass the course, the exact allowed completion methods will be announced when the course starts.

Assessment practices and criteria

The course is graded on a numerical scale from 0 to 5.

Activities and teaching methods in support of learning

Course will be a MOOC course with contents available online, automated home assignment for practical hands-on use of Big Data platforms such as Apache Spark.