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 understanding the underpinnings of, 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


Week Date Topic Teacher Assignment (Deadline)
1 2/9 Course introduction, Big and Fast data, Intro to course PLs GG
1 4/9 The Unix programming environment, Diomidis’s slides DS Unix (16/09)
2 9/9 Programming for Big Data (1) GG
2 11/9 Programming for Big Data (2) GG Functional programming (30/09)
3 16/9 Distributed Systems GG
3 18/9 Distributed Databases GG
4 23/9 Distributed Filesystems GG Distributed Systems (07/10)
4 25/9 Distributed Systems 4 Lecture cancelled MongoDB (Optional)
5 30/9 Spark: RDDs and Pair RDDs GG
5 2/10 Spark Internals GG
6 7/10 Spark SQL, Spark use cases: Synonyms with Word2Vec, Recommending bands, Predicting pull request merges GG Spark (21/10)
6 9/10 Graph Processing GG
7 14/10 Stream processing GG
7 16/10 Stream processing systems GG Flink (01/11)
8 21/10 Data engineering on the cloud GG
8 23/10 No lecture GG
9 28/10 Recap, Answers to recap questions (Quintin van Leersum and Mikhail Epifanov)


The lab work is supervised by Thomas Overklift.

Online Resources

Portions of this course have been converted to online educational material by other TU Delft teachers. Please take a look at the following EdX MOOCs / ProfEds:

Use them at your discretion to improve your skills.

(TU Delft only): You can find the Collegerama recordings from 2019 here. Please note that the course contents have sligthly changed this year, so do not base your exam studying on the old lectures.


You can find the course assignments on Brightspace and linked through this page. There will be 4 assignments instead of 5 due to circumstances; the assignment about distributed systems has been dropped.

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.


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. If you were to buy a single book about this course, I would recommend [1].


M. Kleppmann, Designing data-intensive applications. O’Reilly Media, Inc., 2017.
J. Laskowski, “Mastering apache spark 2,” 2017. [Online]. Available:
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.
H. Karau, A. Konwinski, P. Wendell, and M. Zaharia, Learning spark: Lightning-fast big data analysis. O’Reilly Media, Inc., 2015.
H. Karau and R. Warren, High performance spark. O’Reilly Media, Inc., 2017.
B. Chambers and M. Zaharia, Spark: The definitive guide. O’Reilly Media, Inc., 2017.
T. Akidau, S. Chernyak, and R. Lax, Streaming systems: The what, where, when, and how of large-scale data processing. O’Reilly, 2018.
C. Martella, R. Shaposhnik, D. Logothetis, and S. Harenberg, Practical graph analytics with apache Giraph. Springer, 2015.
I. Robinson, J. Webber, and E. Eifrem, Graph databases: New opportunities for connected data. Springer, 2015.