Testing with Big Data White paper for NTT DATA Gold Club 1.0 10-Mar-2014 REVISION HISTORY Version Effective Date (DD/MM/YYYY) Brief Description of Change Affected Section(s) Prepared By Reviewed By Approved By 1.0 10/03/2014 Varun Rathnakar Varun Rathnakar TABLE OF CONTENTS 1 Introduction 4 2 Characteristics of Big Data 5 3 Big Data Implementation 7 4 Big Data Testing Focus Areas 8 5 Conclusion 12 6 References 13 1 Introduction Big data refers to large datasets that are challenging to store, search, share, visualize, and analyze and so the Testing. Testing of Big Data is one of the toughest since there is a lack of knowledge on what to test and how much to test.Traditional DW testing approach is inadequate due to Technology Changes, Infrastructure (DB/ETL on Cloud) and Big Data. It’s a digital world, which is causing massive increases in the volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources) of data. As a result, concern for realistic data sets, data accuracy, consistency and data quality is now a critical issue. This paper tries to explore testing challenges in Big Data adoption and outline a testing strategy to validate high volume, velocity and variety of information. 2 Characteristics of Big Data Big data refers to data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage and process the data within a tolerable elapsed time.Big Data has vast characteristic and few of them are showed below. Fig 1: Charateristicsof Big Data There are four fundamental characteristics on which the test strategy needs to be defined. These are: • Data Volume: The sheer amount of data that keeps generated by machines, networks... ... middle of paper ... ... testing team validates structured and unstructured data of large volume. Obtaining timely and meaningful information is difficult, if not possible, often hampering decision making and in many cases the cost of obtaining information is prohibitive. Organizations faced with this situation must fully realize the benefits of a big data initiative, create an effective test strategy and appropriate test environment as part of their overall validation strategy. 6 References http://inside-bigdata.com/2013/09/12/beyond-volume-variety-velocity-issue-big-data-veracity/ Big data Overview, Wikipedia.org at http://en.wikipedia.org/wiki/Big_data http://www.ibmbigdatahub.com/infographic/four-vs-big-data#disqus_thread Big data: Testing Approach to Overcome Quality Challenges, Infosys.com at http://www.infosys.com/ infosys-labs/publications/Documents/testing-approach.pdf
Veracity refers to the messiness or trustworthiness of the data. There are many forms of big data quality and accuracy that make it very hard to control. An example of this would be hashtags, abbreviations, or typos. Technology now lets us to work with this type of data.
Data Breach Prevention A data breach is any action or subsidy that results in an individual’s personal information being accessed by an unauthorised entity, and/or when it is lost. Personal information is information regarding an individual, or any information associated with said individual. A large proportion of the information that TEAR harbours is personal data due to the nature of the work which TEAR does. As a result, a data breach within the organisation of TEAR, could result in the loss of a substantial quantity of personal information. There are multiple levels of severity when regarding a data breach.
The key strategy implementation efforts at Amazon all surround the use of “big data”. Big data is the growth and availability of large volumes of structured/unstructured data. The use of big data has allowed decision making based upon data and analysis instead of past experience and intuition. Big data has directed organizational change in allowing Amazon to expand from an online book store to an internet giant. Revolutionary application of big data has allowed Amazon to create superior service quality while motivating employees by providing real time information to solve customer issues. Big data has strengthened Amazon’s competitive capabilities by pioneering the application of big data and charging a monthly fee to smaller businesses
If auditors can look at a complete population, they may not have a great defense if they missed a “smoking gun” since they looked at all the data (Alles and Glen). However, this data may not be valid which raises the importance of the auditor understanding where the data came from and how reliable it is. Not only this, it will be interesting to see how standards consider big data evidence. While it most likely will not be as reliable as confirmations, it would be a challenge to figure out how much the auditors could rely on it. Furthermore, higher education would most likely play a role in helping their graduates understand data and how to use technology to be not only more efficient but also ensure they are able to use sound professional judgement while using big data.
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
Big Data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools. However, Big Data contains a lot of valuable information which if extracted successfully, it will help a lot for business, scientific research, to predict the upcoming epidemic and even determining traffic conditions in real time. Therefore, these data must be collected, organized, storage, search, sharing in a different way than usual. In this article, invite you and learn about Big Data, methods people use to exploit it and how it helps our life.
Currently the world has a wealth of data, stored all over the planet (the Internet and Web are prime examples), but it is needed to be understand that data. It has been stated that the amount of data doubles approximately
Test case output is reviewed to spot any errors that might occur in the integration phase. Developers will then work towards documentation and completion using the test case that was gathered. Test data is merely data that is used in instruction to test the definite program or the integrated components. Test data would generally be used in a test case as this would be used to square the inputs and predict outputs. System Testing System testing is testing the system and its various components.
Big data will then be defined as large collections of complex data which can either be structured or unstructured. Big data is difficult to notate and process due to its size and raw nature. The nature of this data makes it important for analyses of information or business functions and it creates value. According to Manyika, Chui et al. (2011: 1), “Big data is not defined by its capacity in terms of terabytes but it’s assumed that as technology progresses, the size of datasets that are considered as big data will increase”.
Software development follows a specific life cycle that starts with designing a solution to a problem and implementing it. Software testing is part of this software life cycle that involves verifying if each unit implemented meets the specifications of the design. Even with careful testing of hundreds or thousands of variables and code statements, users of software find bugs. “Software testing is arguably the least understood part of the development process” and is also a “time-consuming process that requires technical sophistication and proper planning” (Whittaker 71) It is important to comprehend this concept by understanding the different characteristics and aspects of software testing, and then to examine the techniques, procedures and tools used to apply this concept. This will enable the user to realize the problems faced by software testers and the importance of software testing.
The challenges include capture, storage, search, sharing, transfer, analysis, and visualization. The trend to larger data sets is due to the additional data derivable from analysis of a single large set of related information. As of 2012, limits on the increasing size of data sets that are feasible to process in an efficient amount of time were on the order of terabytes of data. Scientists are regularly trying to find out limitations due to large data sets in many areas, including meteorology, genomics, complex physics simulations, and biological and environmental research. The limitations also affect Internet search, finance and business informatics. Data sets grow in size in part because they are increasingly being collected by universal information-sensing mobile devices, aerial sensory technologies (remote sensing), software records, cameras, microphones, radio-frequency identification experts, and wireless sensor networks. The world's technological per-capita capacity to store data has roughly doubled every 40 months since the 1970s; as of 2010, every day 2.5 terabytes (2.5×1018) of data is being created. The challenge for large enterprises is to determine who should own big information initiatives that stride the entire organization.
Cloud computing is a type of computing that depends on sharing computing resources rather than having local servers or personal device to handle applications.
Information privacy, or data privacy is the relationship between distribution of data, technology, the public expectation of privacy, and the legal and political issues surrounding them.
Adopting big data can also help the banking industry by saving them from lots of embarrassment resulting from increase in the number of customer which in turn requires banks to improve on their performance. As stated earlier banks are entrusted with lots of information and this information must be safe will be required to be accessed ready and in a timely fashion. The use a normal small database will not be enough to perform this operation and if banks don’t embrace the use of big data they might start to experience failure in there system.