The Enterprise Stream Processing Benchmark

Motivation

Why another benchmark?

The ever increasing amount of data that is produced nowadays, from smart homes to smart factories, gives rise to completely new challenges and opportunities. Terms like "Internet of Things" (IoT) and "Big Data" have gained traction to describe the creation and analysis of these new data masses. Furthermore, new technologies and systems were developed that are able to handle and analyze data streams, i.e., data arriving with high frequency and in large volume.

In recent years, e.g., a lot of distributed Data Stream Processing Systems were developed, whose usage represents one way of analyzing data streams. Although a broad variety of systems or system architectures is generally a good thing, the bigger the choice, the harder it is to choose.

Benchmarking is a common and proven approach to identify the best system for a specific set of needs. However, currently, no satisfying benchmark for modern data stream processing architectures exists. Particularly when an enterprise context, i.e., where data streams have to be combined with historical and transactional data, existing benchmarks have shortcomings. The Enterprise Streaming Benchmark (ESB), which is to be developed, will attempt to tackle this issue.

Objectives

We aim to create a relevant, real-world application benchmark with a focus on data stream processing architectures in an enterprise context. This involves including business or transactional data stored in a traditional database into the analysis process. In order to ease usage of the developed benchmark, we will develop a comprehensive toolkit for supporting implementation as well as setup and execution. Particularly, it will comprise tools for system setup, data ingestion, data validation and benchmark result analysis.

With regard to the domain, we will focus on an industrial manufacturing context. So the defined benchmark queries will answer questions in that area. Our primary data source will be sensor data from, e.g., industrial machinery. Therefore, we aim to work with real-world data.

Contact

Contact: Guenter Hesse

Github repository: https://github.com/guenter-hesse/ESPBench

Research Area: In-Memory Data Management for Enterprise Systems

Selected Publications

ESPBench: The Enterprise Stream Processing Benchmark. Hesse, Guenter; Matthies, Christoph; Perscheid, Michael; Uflacker, Matthias; Plattner, Hasso (2021). 201–212.

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How Fast Can We Insert? An Empirical Performance Evaluation of Apache Kafka. Hesse, Guenter; Matthies, Christoph; Uflacker, Matthias (2021). 641–648.

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Application of Data Stream Processing Technologies in Industry 4.0: What is Missing?. Hesse, Guenter; Sinzig, Werner; Matthies, Christoph; Uflacker, Matthias (2019). 304–310.

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Quantitative Impact Evaluation of an Abstraction Layer for Data Stream Processing Systems. Hesse, Guenter; Matthies, Christoph; Glass, Kelvin; Huegle, Johannes; Uflacker, Matthias (2019). 1381–1392.

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Adding Value by Combining Business and Sensor Data: An Industry 4.0 Use Case. Hesse, Guenter; Matthies, Christoph; Sinzig, Werner; Uflacker, Matthias G. Li, J. Yang, J. Gama, J. Natwichai, Y. Tong (reds.) (2019). (Vol. 11448) 528–532.

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Senska - Towards an Enterprise Streaming Benchmark. Hesse, Guenter; Reissaus, Benjamin; Matthies, Christoph; Lorenz, Martin; Kraus, Milena; Uflacker, Matthias (2018). 25–40.

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A New Application Benchmark for Data Stream Processing Architectures in an Enterprise Context: Doctoral Symposium. Hesse, Guenter; Matthies, Christoph; Reissaus, Benjamin; Uflacker, Matthias in DEBS ’17 (2017). 359–362.

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Conceptual Survey on Data Stream Processing Systems. Hesse, Guenter; Lorenz, Martin in IEEE International Conference on Parallel and Distributed Systems (ICPADS) (2015). 797–802.

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The Enterprise Stream Processing Benchmark

Motivation

Why another benchmark?

Objectives

Contact

Selected Publications

Open Positions

News

27.08.2021 | New MOOC "Enterprise Goes Cloud" is Live on openSAP

19.07.2021 | Bachelor Project Presented Their Dashboard Built on SAP Graph

04.05.2021 | "Data dependencies for query optimization: a survey" published in VLDBJ

27.04.2021 | Successful PhD Defense of Ralf Teusner on Programming Education in Massive Open

19.11.2020 | Best Paper Award received at CSEE&T 2020

28.07.2020 | "An Experimental Evaluation of Index Selection Algorithms" published in PVLDB

04.05.2020 | "Quantifying TPC-H Choke Points and Their Optimizations" published in PVLDB

01.05.2020 | Change of Chair Representation

03.03.2020 | Best Master Thesis in Education Technologies 2019

Literature

Contact