Big Data Gets Cloudy: Challenges and Opportunities
Cloud computing and big data are complementary, forming a dialectical relationship. Cloud computing and the widespread use of internet application is the ultimate need of the hour. Though seen as full of promising opportunities, both the fields have their
- PDF / 429,235 Bytes
- 14 Pages / 439.37 x 666.142 pts Page_size
- 59 Downloads / 228 Views
Abstract Cloud computing and big data are complementary, forming a dialectical relationship. Cloud computing and the widespread use of internet application is the ultimate need of the hour. Though seen as full of promising opportunities, both the fields have their own challenges. Cloud computing is a trend in technology development, while big data is an inevitable phenomenon of the rapid development of a modern information society. Modern means like Cloud computing technologies are needed to solve big data problems. With the advent of new technologies in the field of data and computing, innumerable services are emerging on the net, generating huge volume of data. The data so generated is becoming too large and complex to be effectively processed by conventional means. How to store, manage, and create values from this huge ocean of big data has become an important research problem in today’s time. Presently, users are accessing multiple data storage platforms to accomplish their operational and analytical requirements. Efficient integration of different data sources, in the merger of the two technologies, i.e., Big Data and Cloud, poses considerable challenges. Data integration here plays a very important role for both commercial and scientific domains in order to combine data from different sources and provides users with a unified view of these data. Keeping in mind the 4 V’s of Big Data (volume, velocity, variety, and veracity), studying the challenges and opportunities coming in the way of efficient data integration is a key research direction for scientists. This paper will describe • How cloud and big data technologies are converging to offer a cost-effective delivery model for cloud-based big data analytics. • Big Data Challenges. • Challenges in cloud computing. • Challenges when big data moves to cloud. Keywords Big data · Cloud · Hadoop · HDFS · MapReduce · CSP (Cloud Service Providers) · SLA (Service Level Agreements)
P. Joshi (B) Computer Science Department, Birla Institute of Technology Extension Centre Noida, Noida, India e-mail: [email protected] © Springer Science+Business Media Singapore 2016 V.K. Singh et al. (eds.), Modern Mathematical Methods and High Performance Computing in Science and Technology, Springer Proceedings in Mathematics & Statistics 171, DOI 10.1007/978-981-10-1454-3_16
193
194
P. Joshi
Fig. 1 Big data on cloud
1 Introduction Two IT initiatives are currently top of mind for organizations across the globe: big data analytics and cloud computing. Whereas big data analytics offers the promise of providing valuable insights that can create competitive advantage, revolutionize the trends and more turnover by organizations, Cloud computing has the potential to enhance business agility and productivity at reduced cost while enabling greater efficiencies. Both technologies are making rapid progress and a large number of organizations are developing efficient and agile cloud solutions with cloud providers expanding their service offerings. On the other hand, IT organizations are
Data Loading...
