Getting ready for BigData testing: A practitioner's perception
04 Jul 2013-pp 1-5
TL;DR: Two specialized testings are considered to learn the intricacies of Big Test Data Management, and thoughts on Big Test data Management are also presented.
Abstract: Big Data is already there and developers and analytics are working with it using several support upcoming frameworks and technologies. The storage and retrieval systems, the access layers and processes for Big Data are evolving day by day. Test Architects and Testing teams are not excluded in this Big scenario. This literature focuses on some of the challenges test teams would be facing in the near future. Two specialized testings are considered to learn the intricacies, and thoughts on Big Test Data Management are also presented.
Citations
More filters
14 May 2016
TL;DR: It is argued that existing approaches to testing big data applications may not necessarily scale without assistance, and open issues and possible solutions specific to testingbig data applications are discussed.
Abstract: Massive datasets are quickly becoming a concern for many industries. For example, many web-based applications must be able to handle petabytes worth of transactions on a daily basis, and moreover, be able to quickly and efficiently act upon data that exists in each transaction. As a result, providing testing capabilities for such applications becomes a challenge of scale. We argue that existing approaches, such as automated test suite generation, may not necessarily scale without assistance. To this end, we discuss open issues and possible solutions specific to testing big data applications.
3 citations
Cites background from "Getting ready for BigData testing: ..."
...However, there is little published research in performing testing on applications that already interact with big data [9]....
[...]
25 Nov 2022
TL;DR: In this paper , the authors present several performance testing approaches for big data and compare them with various parameters such as domain applicability, scripting interface etc., and suggest tools to improve overall performance.
Abstract: Big data has become the primary objective for any area or department on the globe, including government, healthcare, and industrial sectors. Every day, a large amount of big data is generated, and information is added based on the three characteristics of big data. Handling massive amounts of data with traditional methods has become a significant challenge. To test large amount of duplicate data is very challenging task for researchers. Big data performance testing has great promise for obtaining more optimised solutions. Better performance saves time and money while also producing more optimised results. This paper makes an attempt to present several performance testing approaches. Furthermore, we compare the performance testing techniques and analyse the performance improvement factors. Finally, various tools for performance testing of big data, such as Apache Jmeter, Apache Drill, LoadRunner, WebLoad, YCSB etc., are also compared with various parameters such as domain applicability, scripting interface etc., and tools are suggested to improve overall performance.
25 Nov 2022
TL;DR: In this article , the authors present several performance testing approaches for big data and compare them with various parameters such as domain applicability, scripting interface etc., and suggest tools to improve overall performance.
Abstract: Big data has become the primary objective for any area or department on the globe, including government, healthcare, and industrial sectors. Every day, a large amount of big data is generated, and information is added based on the three characteristics of big data. Handling massive amounts of data with traditional methods has become a significant challenge. To test large amount of duplicate data is very challenging task for researchers. Big data performance testing has great promise for obtaining more optimised solutions. Better performance saves time and money while also producing more optimised results. This paper makes an attempt to present several performance testing approaches. Furthermore, we compare the performance testing techniques and analyse the performance improvement factors. Finally, various tools for performance testing of big data, such as Apache Jmeter, Apache Drill, LoadRunner, WebLoad, YCSB etc., are also compared with various parameters such as domain applicability, scripting interface etc., and tools are suggested to improve overall performance.
References
More filters
[...]
01 Jan 2015
TL;DR: The workshop was hosted by The Law and Technology Centre of the Faculty of Law, The University of Hong Kong, Hong Kong as discussed by the authors, Hong Kong and was presented by the authors of this paper.
Abstract: The Workshop was hosted by The Law and Technology Centre of the Faculty of Law, The University of Hong Kong
541 citations
01 Jan 2012
TL;DR: Preliminary infrastructure tuning results in sorting 1TB data in 14 minutes 1 on 10 Power 730 machines running IBM InfoSphere BigInsights and further improvement is expected, among other factors, on the new IBM PowerLinux TM 7R2 systems.
Abstract: The use of Big Data underpins critical activities in all sectors of our society. Achieving the full transformative potential of Big Data in this increasingly digital world requires both new data analysis algorithms and a new class of systems to handle the dramatic data growth, the demand to integrate structured and unstructured data analytics, and the increasing computing needs of massive-scale analytics. In this paper, we discuss several Big Data research activities at IBM Research: (1) Big Data benchmarking and methodology; (2) workload optimized systems for Big Data; (3) case study of Big Data workloads on IBM Power systems. In (3), we show that preliminary infrastructure tuning results in sorting 1TB data in 14 minutes 1 on 10 Power 730 machines running IBM InfoSphere BigInsights. Further improvement is expected, among other factors, on the new IBM PowerLinux TM 7R2 systems.
9 citations
"Getting ready for BigData testing: ..." refers background in this paper
...Volume is the enormity of data, variety is the heterogeneity of data, velocity is the rate of transfer (speed) of data that comes in, flows within and goes out, and veracity is the truthiness of the data or information [1]....
[...]