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.

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(Avautuu uuteen välilehteen)

Katso englanninkielinen versio.