Welcome to CS 591 K1: Data Stream Processing and Analytics - Spring 2021.

Lectures | Hands-on | Readings | Piazza | Blackboard

Course information

• Course Syllabus
• Code: CS 591 K1
• Instructor: Vasiliki Kalavri
• Lectures: Tue/Thu 9:30-10:45 EST, MCS B31
• Office hours: Mon 9:00-10:00 EST & Fri 9:00-11:00 EST, (Zoom)

Overview

Modern data-driven applications require continuous, low-latency processing of large-scale, rapid data events such as videos, images, emails, chats, clicks, search queries, financial transactions, traffic records, sensor measurements, etc. Extracting knowledge from these data streams is particularly challenging due to their high speed and massive volume.

Distributed stream processing has recently become highly popular across industry and academia due to its capabilities to both improve established data processing tasks and to facilitate novel applications with real-time requirements.

In this course, we will study the design and architecture of modern distributed streaming systems as well as fundamental algorithms for analyzing data streams.

Specifically, we will cover the following topics:

• Distributed streaming systems design and architectures
• Fault-tolerance and processing guarantees
• State management
• Windowing semantics and optimizations
• Basic data stream mining algorithms (e.g. sampling, counting, filtering)
• Query languages and libraries for stream processing
• Streaming applications and use-cases
• Modern streaming systems, such as Apache Flink and Apache Kafka

Prerequisites

To be successful in this class, you will need to have strong programming skills, a solid understanding of Computer Systems fundamentals (e.g. CS210) and some prior experience with Java. Distributed Systems (CS 451/651) and Database Systems (CS 460/660) are highly recommended prerequisites but not blockers. Further skills might be required for some semester projects. Details will be included in the corresponding project descriptions.

Grading scheme

Your final grade will be determined by (1) your participation & effort, (2) written assignments, and (3) your semester project deliverables. There is no formal examination at the end of the course. See the Syllabus for the detailed grading scheme.