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Readings

Below you can find a list of recommended readings and resources the lectures are based on. Parts of the resources listed here will be given as (non-graded) reading assignments during the course. You should be able to access all of these for free when connected to the campus network. Please contact the instructor if any of the links below is broken.

Books

• Data Stream Management: Processing High-Speed Data Streams
• Introduction to Apache Flink: Stream Processing for Real Time and Beyond
• Mining of Massive Datasets, Chapter 4

Articles and papers

• The 8 Requirements of Real-Time Stream Processing
• Discretized streams: fault-tolerant streaming computation at scale
• Flexible Time Management in Data Stream Systems
• Shrink: prescribing resiliency solutions for streaming
• Three steps is all you need: fast, accurate, automatic scaling decisions for distributed streaming dataflows
• Chain: Operator Scheduling for Memory Minimization in Data Stream Systems
• Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems
• Partitioning Functions for Stateful Data Parallelism in Stream Processing
• A Catalog of Stream Processing Optimizations
• SECRET: a model for analysis of the execution semantics of stream processing systems
• Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
• SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection
• Online Event Processing: Achieving consistency where distributed transactions have failed

Blog posts

• Streaming 101: The world beyond batch
• Streaming 102: The world beyond batch
• Watermarks, Tables, Event Time, and the Dataflow Model
• Kafka streams take on watermarks and triggers
• Why I Recommend My Clients NOT Use KSQL and Kafka Streams
• A Deep-Dive into Flink’s Network Stack
• Managing Large State in Apache Flink: An Intro to Incremental Checkpointing
• An Overview of End-to-End Exactly-Once Processing in Apache Flink (with Apache Kafka, too!)
• A Deep Dive into Rescalable State in Apache Flink
• How To Size Your Apache Flink Cluster: A Back-of-the-Envelope Calculation
• The Curious Case of the Broken Benchmark: Revisiting Apache Flink vs. Databricks Runtime
• Tracking progress in Timely Dataflow