Overview

The realm of streaming systems is a rapidly evolving field, focused on the ability to process and analyze data in real-time. The collection of over 20 significant papers highlights key advancements and methodologies within this area, covering essential aspects such as design, implementation, fault recovery, and elastic scaling. This curated list reflects research and innovation, tracing the development of unified models for streaming and batch systems since 2014.

From this selection, notable papers like those discussing Drizzle, Apache Flink, and Twitter Heron illuminate various approaches to improving stream processing capabilities. Each contribution not only addresses existing challenges but also offers practical solutions, making this resource invaluable for anyone interested in streaming data technologies.

Features

Comprehensive Paper List: Over 20 key papers covering diverse topics in streaming systems, ideal for researchers and practitioners wanting to stay informed.
Unified Models: Papers discussing the integration of streaming and batch processing models since 2014, paving the way for more efficient data handling and analysis.
Drizzle Innovations: Introduces improvements to traditional systems like Spark Streaming by combining group and pre-scheduling shuffles for enhanced performance.
Facebook's Real-Time Processing: Insights into custom solutions built by Facebook, designed for minimal latency in high-demand scenarios.
Apache Flink: An open-source framework that seamlessly processes both streaming and batch data, supporting a variety of analytic and continuous data processing applications.
Lightweight Snapshots: Explores how Apache Flink ensures distributed state consistency and exactly-once processing semantics through innovative landmark techniques.
Twitter Heron: A real-time processing alternative to Storm, addressing scalability and performance challenges while maintaining compatibility.
Trill's High Performance: Presents a high-performance incremental query processor that bridges the gap between real-time and offline data processing needs effectively.

Lw Lin

Streaming Readings