Course Information

Coming soon (fall 2022)

Big Data platforms is a 5 ECTS Master's level advanced course. This course focuses on big data platforms and on key algorithmic ideas and methods used to implement them. After completing this course you are able to list many of the key technologies used in big data processing and to select suitable methods for solving challenging big data processing tasks using cloud computing technologies. You will also be able to compare the scalability and fault tolerance implications of using the selected methodologies.

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 (PySpark) computing frameworks, and
  • distributed cloud based databases.

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

Course Target Audience

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. Optional course in Data Science Master's Program. Also suitable for Computer Science Master's Program students. The course is suitable to University of Helsinki exchange students.

Course Prerequisites

To attend this course, you must have:

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

Lecture Schedule

The Lectures of the course will be will Zoom based lectures. Slides and video recording of each of the lectures will be made available a wuithin 24 hours of the live lecture session. The link to the Zoom lectures is:

https://helsinki.zoom.us/j/67373457349?pwd=N3V2dzNrNjg0aWg3Ukk4Wi8yVGN2dz09

Lecture date Lecture time (EEST)
Lecture 1 Tue 6.9.2022 10:15-11:45
Lecture 2 Thu 8.9.2022 12:15-13:45
Lecture 3 Tue 13.9.2022 10:15-11:45
Lecture 4 Thu 15.9.2022 12:15-13:45
Lecture 5 Tue 20.9.2022 10:15-11:45
Lecture 6 Thu 22.9.2022 12:15-13:45
Lecture 7 Tue 27.9.2022 10:15-11:45
Lecture 8 Thu 29.9.2022 12:15-13:45
Lecture 9 Tue 4.10.2022 10:15-11:45
Lecture 10 Thu 6.10.2022 12:15-13:45
Lecture 11 Tue 11.10.2022 10:15-11:45
Backup Lecture Slot 1 Thu 13.10.2022 12:15-13:45
Backup Lecture Slot 2 Tue 18.10.2022 10:15-11:45

Lecture Slides and Videos

The Lecture slides contain all the material needed to pass the course, the videos go through this material and contain no additional information needed for the quizzes.

Lecture Slides Lecture Videos
Lecture 1
Lecture 2
Lecture 3
Lecture 4
Lecture 5
Lecture 6
Lecture 7
Lecture 8
Lecture 9
Lecture 10
Lecture 11

Home Exercise Schedule

The course will contain programming exercises where you will be using the Spark framework to solve Big Data processing tasks. We will be using the Python programming language based PySpark interface and will be doing several database query type analytics queries. Therefore basic programming skills using Python and knowledge about database programming, especially using the SQL query language will be very helpful for completing the home exercises.

The schedule for the home exercises will be announced in the first Lecture:

Release Date Due Date (23:59 EEST)
Introduction to Spark + RDD Programming
Dataframe Programming
Machine Learning (MLlib)
Graphframe Programming
Structured Streaming
Extras (Optional for extra points)

The Home Exercise System

To complete the exercises you will have to use the JupyterHub notebook platform.

A short introduction video on how to use the platform to complete and submit the exercises can be found at:

https://youtu.be/F0mjmycxWUg.

The assignment grades take a few hours to get published/updated and are not instant at the moment. We are running the assignment grading scripts once per hour, the system doesn't allow instant grading when a student submits an exercise. Therefore, please be aware of that and give some time before you check back on your assignment grades. Once the grades are assigned, you can use the "fetch feedback" feature to find out which exercises failed (if any). Note that there are additional tests run on the server side that affect the grading scores. If you want, you can resubmit each exercise several times, the best obtained score is recorded to the course points for each exercise.

Make sure to use the assignment submission validation feature before your submissions if you want to gain more confidence about your submission's correctness and final grade. Note that the validation feature can be slow, you can also run your tests seperately by running the respective test cells. If you can run through the whole notebook without getting validation errors, all the tests are successful.

For any of your generic course-related questions and group discussions, use our Telegram group. Try to use the group to help each other out and discuss exercise and course-related issues. We will also periodically check the group to address the more important unanswered issues.

Course Telegram Group

The course has a Telegram group for helping fellow students. Lecturer and Course Assistant will periodically also join in the conversation. You can join to the groups through the link:

https://t.me/+wMkXFREnybQxMTRk

Passing the Course

Information about passing the course will come in the first Lecture.