Beyond Analytics: The Evolution of Stream Processing Systems

Paris Carbone, Marios Fragkoulis, Vasiliki Kalavri, Asterios Katsifodimos

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

24 Citations (Scopus)

Abstract

Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts by the research community and numerous worldwide open-source communities. The goal of this tutorial is threefold. First, we aim to review and highlight noteworthy past research findings, which were largely ignored until very recently. Second, we intend to underline the differences between early ('00-'10) and modern ('11-'18) streaming systems, and how those systems have evolved through the years. Most importantly, we wish to turn the attention of the database community to recent trends: streaming systems are no longer used only for classic stream processing workloads, namely window aggregates and joins. Instead, modern streaming systems are being increasingly used to deploy general event-driven applications in a scalable fashion, challenging the design decisions, architecture and intended use of existing stream processing systems.

Original languageEnglish
Title of host publicationSIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
PublisherAssociation for Computing Machinery (ACM)
Pages2651-2658
Number of pages8
ISBN (Electronic)9781450367356
DOIs
Publication statusPublished - 14 Jun 2020
Event2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020 - Portland, United States
Duration: 14 Jun 202019 Jun 2020

Conference

Conference2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020
Country/TerritoryUnited States
CityPortland
Period14/06/2019/06/20

Keywords

  • cloud computing
  • distributed computing
  • MapReduce
  • shared-nothing architectures
  • stream processing

Fingerprint

Dive into the research topics of 'Beyond Analytics: The Evolution of Stream Processing Systems'. Together they form a unique fingerprint.

Cite this