Course Introduction

In this course, we will learn

• Programming techniques, specifically functional programming, useful for Big Data Systems.
• Basic distributed systems theory, applied on databases and file systems
• “Small” data processing
• Batch processing
• Stream processing
• Graph processing

Tools and technologies

During our course we will learn how to process data with

• Unix, the de-facto standard for small-ish data
• Spark, and related libraries, for batch processing
• Flink for stream processing
• Spark and custom code for graphs

The course will be using the following 2 programming languages

• Bash: for automating data processing
• Scala: the de-facto standard for high performance data processing

Schedule

You can get information about the course schedule here.

Grades

• 60% final exam, multiple-choice and some open-ended questions.
• 40% assignments. 4 assignments, 10% each.

There will be an exam resit during Q2/3. You are allowed to transfer your assignment grade to the resit as a whole.

If the final assignment grade is in between 4 and 5.7, one of the previously submitted assignments can be repaired.

Partial grades (assignments or exam) cannot be transferred to subsequent years.

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER).

Assignments

• The course consists of 4 mandatory assignments.

• You always work in pairs: select your team mate!

• Grade: \(\frac{\sum_{assign=1}^{4} grade(assign)}{4}\), aka the mean

  • No submission? Grade \(\rightarrow\) 0
  • Late submission? Grade \(\rightarrow\) 0

• We will use Unix for the first assignment, IntelliJ/Scala for all others.

• Template files are provided on Brightspace

  • Use Maven for assignments 2, 3, and 4 to install all dependencies

Assignment submission

We will use Weblab. The course name is CSE2520: Big Data Processing

• You need to signup to enroll and also declare your pairs
• To submit, hit the overview button and select the appropriate assignment
  • Pay attention to the instructions about submitting an assignment. Deviating from these may cause automatic grading to fail
• All the assignments have deadlines
• Feedback and grading is automatic: the results are available on Weblab.
• Technical support: ask the TAs during the labs or via Mattermost

Need help?

• Lab sessions every Wednesday afternoon
  • All TAs will be around
  • Work together to solve assignments
  • Ask questions about the next assignment
  • Ask questions about your grades
• You are welcome to join the BDP 2023-2024 Mattermost team.

Rules for using Mattermost

• Fire and forget
  • Don’t expect TAs to answer
  • Don’t expect the course instructors to answer questions
  • TAs and instructors do not work outside business hours
• Mattermost should be mostly used to exchange opinions and ideas
  • Not assignment solutions
• No abusive behavior (swearing, stalking, threatening, sexism etc) will be tolerated

Slide symbols

• Q A question with a known answer; this will be revealed, but we should work together towards it!

• D A open discussion item; we need to think and discuss.

Lecture notes

• Freely available on the web

• You can print/download them before the lecture and bring them along to make additional notes.

• We are looking forward to improve them! If you have suggestions, find errors etc, let us know!

Bibliography

Copyright

This work is (c) 2017, 2018, 2019, 2020, 2021 - onwards by TU Delft and Georgios Gousios and licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license.