General description

The term “Big Data” describes datasets that are either too big or change too fast or both to be processed on a single computer.

Big Data Processing provides an introduction to systems used to process Big Data. The main focus of the course is programming and engineering big data systems; initially, the course explores general programming primitives that span across big data systems and touches upon distributed systems. Then, the course examines in detail the implementation of data analysis algorithms in Spark, in the context of batch processing applications, and Flink, in the context of streaming applications.

Learning objectives

After the end of the course, all students should be able to:

Course Organization

Contents

Week Date Topic Teacher Assignment (Deadline)
1 4/9 Course introduction, Big and Fast data, Intro to course PLs GG
2 10/9 Distributed Systems JR
2 11/9 Distributed Databases, Distributed filesystems JR
3 17/9 The Unix programming environment GG Unix
(1/10)
3 18/9 Programming for Big Data (1) GG
4 24/9 Programming for Big Data (2) GG Functional programming (8/10)
4 25/9 Spark: RDDs and Pair RDDs GG
5 1/10 Spark Internals GG
5 2/10 Spark SQL, Spark use cases: Synonyms with Word2Vec, Recommending bands, Predicting pull request merges GG Spark (21/10)
6 8/10 Stream processing GG
6 9/10 Stream processing systems GG Streaming (31/10)
7 15/10 Graph Processing GG
7 16/10 GG
9 29/10 Recap, Answers to recap questions (Quintin van Leersum and Mikhail Epifanov)

Teachers

  • GG: Georgios Gousios
  • JR: Jan Rellermeyer

TAs

The head TA is Jeroen Galjaard.

Assignments

You can find the course assignments linked through this page.

All assignments are mandatory.

For submission, we will use CPM. The course name is CSE2520: Big Data Processing

The student groups must submit each assignment before 23:59 on the day of the deadline.

Late submission: All submissions must be handed in time, with no exceptions. Any late submission will be discarded and will be graded with 0. In case of provable sickness, please contact the course teacher to arrange a case-specific deadline.

Assessment

Example exam material

Resit policy

There will be an exam-only resit during Q2/3. You are allowed to transfer your assignment grade to the resit as a whole. This means that you will not be able to re-submit individual assignments. Effectively, you can only resit your written exam.

Course resources


The course, by design, touches upon various current technologies; as such, there is no single source of truth. The following is an indicative list of resources where more information can be found.

Bibliography

[1] I. Robinson, J. Webber, and E. Eifrem, Graph databases: New opportunities for connected data. Springer, 2015.

[2] C. Martella, R. Shaposhnik, D. Logothetis, and S. Harenberg, Practical graph analytics with apache Giraph. Springer, 2015.

[3] S. Ryza, U. Laserson, S. Owen, and J. Wills, Advanced analytics with spark: Patterns for learning from data at scale. O’Reilly Media, Inc., 2015.

[4] H. Karau, A. Konwinski, P. Wendell, and M. Zaharia, Learning spark: Lightning-fast big data analysis. O’Reilly Media, Inc., 2015.

[5] B. Chambers and M. Zaharia, Spark: The definitive guide. O’Reilly Media, Inc., 2017.

[6] M. Kleppmann, Designing data-intensive applications. O’Reilly Media, Inc., 2017.

[7] H. Karau and R. Warren, High performance spark. O’Reilly Media, Inc., 2017.

[8] J. Laskowski, “Mastering apache spark 2,” 2017. [Online]. Available: https://www.gitbook.com/book/jaceklaskowski/mastering-apache-spark/details.

[9] T. Akidau, S. Chernyak, and R. Lax, Streaming systems: The what, where, when, and how of large-scale data processing. O’Reilly, 2018.