Impact of big data on the current software world

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Introduction

The term “Big data” entails the large volumes of datasets that typical database software tools have no enough ability to capture, analyze, store and manage. Data is becoming very crucial to every area in our economy. There is a burgeoning of volumes of data in usage by businesses in their day-to-day transactions that includes the customer data, suppliers, and operational data . However, the impact of data volume is far beyond the business functions. Big data is fostering productivity growth across the globe as it has an impact on intensive software industries and in public services. Big data has an impact on software services, and it helps to generate values in this case through the supporting of innovative ecosystems and enablement of new solutions that were not possible in the past. Modern products embed software or their management has a basis on software[1]. Containing software challenges using advanced software development techniques and tools is indispensable for the Software Engineering industries to remain competitive in products and services delivery. Application of big data is essential for software development as it helps in dealing with increasing software complexity.

Big data impact

The continuous growth of data volume makes it possible to have better accessibility for software development tasks. There s a large amount of data available everywhere for software engineering, and the software developers have a wide choice from where to choose. There is developed software that helps them to make reference and, therefore, understand how the software development can take place. One crucial of Software Engineering about big data has a relation the software systems design. Making use of the big data technologies to design as well as build large data systems brings about a software challenge in terms of architecture. The challenges come from the way software architects should deal with the issues that exist in distributed systems. There are challenges such as replication of data, communication latencies, temporary failures as well as concurrent processing. There can be a solution through a big data context whereby systems have to grow so as to make use of the geographically distributed data (Bellavistaet al., 2014).

There is also another impact on big data about scalable software architectures. There is the challenge of how to take care of critical issues of scalability, availability as well as a performance that become essential when one is handling big data systems. There is difficulty in dealing with such systems and applications for them to cope with the unprecedented data size, noise of data and diversity. The way to support such jobs and develop new architectures that amalgamate classical relational DBMS techniques from data storage or data querying is a challenging task. A new generation of software design is in the requirement for optimizing the querying and retrieval of big data. Due to the large quantities of data that is quite heterogeneous at the same time, it becomes very difficult to test environments that fully covers and validates software before deployment.

Big data systems are attaining deployment scales that are changing the way software engineering is taking place. The more the software of components that is available with big data systems, the high the likelihood of failures. The realistic testing of loads becomes obsolete as the test data on which the code runs grows more than the expectations. There will be need to integrate powerful monitoring as well as analysis capabilities into the software applications and deployment infrastructure. Careful monitoring of the behavior of systems with increasing databases, codes and deployment scale can make it possible to handle more easily the failures or proactively take actions.

It is very costly to build data software that has high performance and availability since data processing capabilities double every year. Software products’ performance and availability need to be stable even with the changes in the processing power. It has to handle the big data that continues to increase every second and still be stable in performance, and that is a very challenging activity[2]. The increase of data makes software products to be slower in performance because the processing of larger amounts of data than before requires more powerful software. That is why there have to be software upgrades now and then so as to keep pace with the increased data volume and to maintain high performance and availability. The building of such software with the future in mind is a costly undertaking of which the software engineers need to be aware. That is because the associated cost double if the sustenance of the expected growth has to be in provision.

The software developers, therefore, have the mandate of directly addressing and minimizing the costs associated with the rapid growth in capacity. For instance, they should select a database that can have minimal manual intervention in expanding it. That will make it possible for the data capacity be a fixed value and also to be a low costly undertaking. The accessing of historical data need to have provision for summarized views so as to have rapid access, and it should be in storage in media that have low cost. There is a need, therefore, to store the historical data in data stores that are online. It is because any software uses data, and it entails accessing of numerous amounts of data that is available on the different media globally. Scalable software products should continue to look for and implement acceptable efficiencies so as to make sure that costs grow as slowly as possible.

The need for software applications is to offer the users with the data they need regardless of time and location. There is a need to analyze the big data within a short time and thus make the users have confidence in the product. For instance, the mobile devices need to have software that if fit for the mobile platform and the one that can present all the necessary information that the user may require. The software that handles enabling the user to connect to other systems in a network or Internet should have the mechanisms to filter the data. They should have real-time data management using best methods of data streamlining and data cleaning. Therefore, the software products for these devices need to have those capabilities of enabling high performance with better management of data.

With the current trends in technology, big data is making the software engineering to be faster and smoother than ever. There is a time when software development was not as faster as it is today. Nowadays big data makes the development smoother and faster, thus accelerating the development of software. The data to have an application, however, can sometimes not deliver the required results from the software development. That is because big data consists of both true information and the information that is not true depending on the source of the information and the intended purpose of writing the information. However, the proper usage and the usage of the correct information from big data can lead to early ideation, testing and early implementation of software.

Big data analytics is very useful in solving many problems that exist in software engineering. In that case, big data helps in the better understanding of user needs and understanding areas of the development that should have adaptation or the ones that should not. Big data can help in the performance of cause analysis of software failures by enabling the mining of memory dumps of the software products that are quite complex. It helps in the project organization and the also the decisions on the software product required to solve problems at hand.

Conclusion

As demonstrated throughout this paper, big data impacts software in various ways, some of which are positive and other negative. It essentially makes available a volume of knowledge derived from various sources and this aid in the decision-making in the software production. Software products have to have the mechanisms of carrying out data analysis because of the enormous amounts of data that they need to present to the users of those software products. Big data offers a great deal of knowledge on how to solve software development challenges.

References

Ganore, Pravin, “Positive and negative impacts of big data,” 2014, available online:

Maclver, Kenny, “The big impact of big data on business and society,” last modified November, 2014,


[1] Maclver, Kenny, “The big impact of big data on business and society,” last modified November, 2014,

[2] Ganore, Pravin, “Positive and negative impacts of big data,” 2014, available online: Carolyn Morgan is the author of this paper. A senior editor at MeldaResearch.Com in legitimate essay writing service. If you need a similar paper you can place your order from research paper services.

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