Testing Big Data Applications

Big Data, the new buzz word in the industry, is data that exceeds the processing and analytic capacity of conventional database systems within the time necessary to make them useful. With multiple data stores in abundant formats, billions of rows of data with hundreds of millions of data combinations and the urgent need of making best possible decisions, the challenge is big and the solution bigger, Big Data. Comes with it, new advances in computing technology together with its high performance analytics for simpler and faster processing of only relevant data to enable timely and accurate insights using data mining and predictive analytics, text mining, forecasting and optimization on complex data to continuously drive innovation and make the best possible decisions. While Big Data provides solutions to complex business problems like analyzing larger volumes of data than was previously possible to drive more precise answers, analyzing data in motion to capture opportunities that were previously lost, it poses bigger challenges in testing these scenarios. Testing such highly volatile data, which is more often than not unstructured generated from myriad sources such as web logs, radio frequency Id (RFID), sensors embedded in devices, GPS systems etc. and mostly clustered data for its accuracy, high availability, security requires specialization. One of the most challenging things for a tester is to keep pace with changing dynamics of the industry. While on most aspects of testing, the tester need not know the technical details behind the scene however this is where testing Big Data Technology is so different. A tester not only needs to be strong on testing fundamentals but also has to be equally aware of minute details in the architecture of the database designs to analyze several performance bottlenecks and other issues. Like in the example quoted above on In-Memory databases, a tester would need to know how the operating systems allocate and de-allocate memory and understand how much memory is being used at any given time. So, concluding, as the data-analytics Industry evolves further we would see the IT Testing Services getting closely aligned with the Database Engineering and the industry would need more skilled testing professional in this domain to grab the new opportunities.

www.ijarcs.info, 2014

Big data is defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value from it by capturing and analysis process. Big data due to its various properties like volume, velocity, variety, variability, value, complexity and performance put forward many challenges. Many organizations are facing challenges in facing test strategies for structured and unstructured data validation, setting up optimal test environment, working with non relational database and performing non functional testing. These challenges cause poor quality of data in production, delay in implementation and increase in cost. Map Reduce provides a parallel and scalable programming model for data-intensive business and scientific applications. To obtain the actual performance of big data applications, such as response time, maximum online user data capacity size, and a certain maximum processing capacity.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022

Big Data has increased much focus from the scholastic world and the IT business. In the advanced and figuring world, all together is created and gathered at a rate that quickly surpasses the limit go. Right now, more than 2 billion individuals worldwide are associated with the Internet, and more than 5 billion people possess cell phones. By 2020, 50 billion gadgets are relied upon to be associated with the Internet. Now, anticipated information creation will be 44 times more prominent than that in 2009. As data is exchanged and shared at light speed on optic fiber and remote systems, the volume of information and the speed of market development increment. In any case, the quick development rate of such substantial information creates various difficulties, for example, the fast development of information, exchange speed, different information, and security. In any case, Big Data is still in its outset arrange, and the space has not been checked on all in all. Distributed computing has opened up new open doors for testing offices. New innovation and social network patterns are making an ideal tempest of chance, empowering cloud to change inside tasks, Customer connections and industry esteem chains. To guarantee high caliber of cloud applications being worked on, designer must perform testing to analyze the quality and exactness whatever they plan. In this examination paper, we address a testing natural engineering with important key advantages, to perform execution of experiments and utilized testing strategies to improve nature of cloud applications.

Big Data has emerged as an asset for business practices and marketing strategies due to its data analysis capabilities. For an effective decision-making process, reliable data is critically important. Unreliable data results in wrong decision making ultimately leading to a negative reputation, customer churn, and financial loss. Therefore, testing Big Data is vital to ensure the reliability of data. Defining test strategies and setting up a testing environment are complex and challenging tasks. In this research, we tend to focus on data reliability, since a wrong decision based on unreliable data can have huge negative consequences. The testing framework for Big Data is validated via an exploratory case study in the telecommunication sector. Three-layer architecture for Big Data testing is used, namely Ingestion, Preparation, and Access. Errors are injected to verify fault injection in the ingestion and processing of data files of the Hadoop framework and technology stack. The resul...

Big Data has increased much focus from the scholastic world and the IT business. In the advanced and figuring world, all together is created and gathered at a rate that quickly surpasses the limit go. Right now, more than 2 billion individuals worldwide are associated with the Internet, and more than 5 billion people possess cell phones. By 2020, 50 billion gadgets are relied upon to be associated with the Internet. Now, anticipated information creation will be 44 times more prominent than that in 2009. As data is exchanged a nd shared at light speed on optic fiber and remote systems, the volume of information and the speed of market development increment. In any case, the quick development rate of such substantial information creates various difficulties, for example, the fast development of information, exchange speed, different information, and security. In any case, Big Data is still in its outset arrange, and the space has not been checked on all in all. Distributed computing has opened up new open doors for testing offices. New innovation and social network patterns are making an ideal tempest of chance, empowering cloud to change inside tasks, Customer connections and industry esteem chains. To guarantee high caliber of cloud applications being worked on, designer must perform testing to analyze the quality and exactness whatever they plan. In this examination paper, we address a testing natural engineering with important key advantages, to perform execution of experiments and utilized testing strategies to improve nature of cloud applications.

ArXiv, 2021

Big Data is reforming many industrial domains by providing decision support through analyzing large volumes of data. Big Data testing aims to ensure that Big Data systems run smoothly and error-free while maintaining the performance and quality of data. However, because of the diversity and complexity of data, testing Big Data is challenging. Though numerous researches deal with Big Data testing, a comprehensive review to address testing techniques and challenges is not conflate yet. Therefore, we have conducted a systematic review of the Big Data testing techniques period (2010 2021). This paper discusses the processing of testing data by highlighting the techniques used in every processing phase. Furthermore, we discuss the challenges and future directions. Our finding shows that diverse functional, non-functional and combined (functional and non-functional) testing techniques have been used to solve specific problems related to Big Data. At the same time, most of the testing chal...

The fast development in Big Data, just as the extension in data analytics platforms lately, for example, Hadoop and NoSQL, are making new opportunities for cloud computing. Open cloud providers, for example, Amazon Web Services, Google, and Microsoft offer their own brands of the big data system in their cloud, regardless of whether NoSQL or SQL, that is most proficient and effortlessly versatile for companies all things are considered. The majority of this direct us toward the equal connection among cloud and Big Data that is driven by consumer demand for greater, better, and quicker applications. Truth be told, the combination of Big Data and distributed or cloud computing has prompted another services model referred to as Analytics as a Service (AaaS). This model will give association speedier, adaptable approaches to incorporate, analyze, change, and imagine different kinds of structured, semi-structured, and unstructured data in progressively.

As more and more Big Data applications are becoming the industry adopted standard and in order to enable economy of scale, are being fully automated, less and less human involvement is required. It becomes increasingly important to ensure that automated Big Data processes are operating correctly and ensure that organizations and individuals whose lives are impacted by its algorithms are treated fairly. This paper attempts to establish how the designers, architects, system analysts, testers, business analysts, IT auditor can benefit from a generalized approach towards establishing a quality assurance framework for big data quality assurance testing. Testing big data is one of major challenges industry is facing now a days as organizations struggle to decide upon amount of testing required on target data. This results in undesired data being processed to production leading to more cost and time. To overcome this more defined approach is required for validation and verification of data early in the lifecycle.

2018

paper presented at the Maritime Big Data Workshop, 9-10 May 2018, La Spezia, Italy

International Journal of Computer Applications, 2015

New invention of advanced technology, enhanced capacity of storage media, maturity of information technology and popularity of social media, business intelligence and Scientific invention, produces huge amount of data which made ample set of information that is responsible for birth of new concept well known as big data. Big data analytics is the process of examining large amounts of data. The analysis is done on huge amount of data which is structure, semi structure and unstructured. In big data, data is generated at exponentially for reason of increase use of social media, email, document and sensor data. The growth of data has affected all fields, whether it is business sector or the world of science. In this paper, the process of system is reviewed for managing "Big Data" and today's activities on big data tools and techniques.