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Erschienen in:
Reinventing the TPC: From Traditional to Big Data to Internet of Things Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Big Data Benchmark Compendium

verfasst von : Todor Ivanov, Tilmann Rabl, Meikel Poess, Anna Queralt, John  Poelman, Nicolas Poggi, Jeffrey Buell

Erschienen in: Performance Evaluation and Benchmarking: Traditional to Big Data to Internet of Things

Verlag: Springer International Publishing

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Abstract

The field of Big Data and related technologies is rapidly evolving. Consequently, many benchmarks are emerging, driven by academia and industry alike. As these benchmarks are emphasizing different aspects of Big Data and, in many cases, covering different technical platforms and uses cases, it is extremely difficult to keep up with the pace of benchmark creation. Also with the combinations of large volumes of data, heterogeneous data formats and the changing processing velocity, it becomes complex to specify an architecture which best suits all application requirements. This makes the investigation and standardization of such systems very difficult. Therefore, the traditional way of specifying a standardized benchmark with pre-defined workloads, which have been in use for years in the transaction and analytical processing systems, is not trivial to employ for Big Data systems. This document provides a summary of existing benchmarks and those that are in development, gives a side-by-side comparison of their characteristics and discusses their pros and cons. The goal is to understand the current state in Big Data benchmarking and guide practitioners in their approaches and use cases.

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Vorheriges Kapitel Rethinking Benchmarking for Data
Nächstes Kapitel Profiling the Performance of Virtualized Databases with the TPCx-V Benchmark
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Metadaten
Titel
Big Data Benchmark Compendium
verfasst von
Todor Ivanov
Tilmann Rabl
Meikel Poess
Anna Queralt
John  Poelman
Nicolas Poggi
Jeffrey Buell
Copyright-Jahr
2016
Verlag
Springer International Publishing
Buch
Performance Evaluation and Benchmarking: Traditional to Big Data to Internet of Things

Print ISBN: 978-3-319-31408-2

Electronic ISBN: 978-3-319-31409-9

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-31409-9_9

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