A Contemplating approach for Hive and Map reduce for efficient Big Data Implementation

• Autorzy: Sasubilli G. , Sekhar U. S. , Sharma S. , Sharma S.

Identyfikatory

DOI

10.15439/2017KM20

• Konferencja: International Conference on Information Technology and Knowledge Management (1 ; 22-23.12.2017 ; New Delhi, India)
• Języki publikacji: EN

Abstrakty

EN

In the reference current scenario, data is incremented exponentially and speed of data accruing at the rate of petabytes. Big data defines the available amount of data over the different media or wide communication media internet. Big Data term refers to the explosion in the quantity (and quality) of available and potentially relevant data. On the basis of quantity amount of data are very huge and this quantity has been handled by conventional database systems and data warehouses because the amount of data increases similarly complexity with it also increases. Multiple areas are involved in the production, generation, and implementation of Big Data such as news media, social networking sites, business applications, industrial community, and much more. Some parameters concern with the handling of Big Data like Efficient management, proper storage, availability, scalability, and processing. Thus to handle this big data, new techniques, tools, and architecture are required. In the present paper, we have discussed different technology available in the implementation and management of Big Data. This paper contemplates an approach formal tools and techniques used to solve the major difficulties with Big Data, This evaluate different industries data stock exchange to covariance factor and it tells the significance of data through covariance positive result using hive approach and also how much hive approach is efficient for that in the term of HDFS and hive query. and also evaluates the covariance factors after applying hive and map reduce approaches with stock exchange dataset of around 3500. After process data with the hive approach we have conclude that hive approach is better than map reduce and big table in terms of storage and processing of Big Data.

Słowa kluczowe

EN

big data; hadoop; mapreduce; HDFS; grid computing; big table; hive

PL

zbiór danych; hadoop; mapreduce; HDFS; przetwarzanie siatkowe

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2018
• Tom: Vol. 14
• Strony: 131--135
• Opis fizyczny: Bibliogr. 13 poz., il.

Twórcy

autor

Sasubilli G.

• Mc Donald’s, IL.

autor

Sekhar U. S.

• EY

autor

Sharma S.

• ACERC Ajmer Rajasthan, India

autor

Sharma S.

• ACERC Ajmer Rajasthan, India

Bibliografia

• 1. Lawal Muhammad Aminu, “Implementing Big Data Management on Grid Computing Environment”, International Journal of Engineering and Computer Science ISSN: 2319-7242, Volume 3, Issue 9, September 2014, Page No. 8455-8459
• 2. Agrawal et al., 2011; Baer et al., 2011 Agrawal, D., Das, S., &Abbadi, A. (2011), Big Data and Cloud Computing: Current State and Future Opportunities. ACM EDBT Conference, March 22–24, 2011, Uppsala Sweden. http://dx.doi.org/10.1145/1951365.1951432
• 3. Baer, T. (2011). 2012 Trends to Watch: Big Data. Ovum Report, OI00140-041.Baer, T., Sheina, M., and Mukherjee, S. (2011). What is big data? The big architecture.Ovum Report, OI00140-033.
• 4. S. Vikram Phaneendra & E. Madhusudhan Reddy “Big Data- solutions for RDBMS problems- A survey” In 12th IEEE/IFIP Network Operations & Management Symposium (NOMS 2010) (Osaka, Japan, Apr 19{23 2013).
• 5. Kiran kumara Reddi & Dnvsl Indira “Different Technique to Transfer Big Data: survey” IEEE Transactions on 52(8) (Aug.2013) 2348 {2355}
• 6. Jimmy Lin “MapReduce Is Good Enough?” The control project.IEEE Computer 32 (2013)
• 7. Umasri M. L, Shyamalagowri. D, Suresh Kumar. S “Mining Big Data:- Current status and forecast to the future” Volume 4, Issue 1, January 2014 ISSN: 2277 128X.
• 8. Albert Bifet “Mining Big Data in Real Time” Informatica 37 (2013) 15–20 DEC 2012
• 9. Zan Mo, Yanfei Li Research of Big Data Based on the Views of Technology and Application American Journal of Industrial and Business Management, 2015, 5, 192-197 Published Online April 2015 in SciRes.
• 10. Harshawardhan S. Bhosale1, Prof. Devendra P. Gadekar2 “A Review Paper on Big Data and Hadoop” International Journal ofScientific and Research Publications, Volume 4, Issue 10, October 2014 1 ISSN 2250-3153
• 11. https://www.quora.com/What-are-the-main-features-of-Hadoop
• 12. https://www.slideshare.net/sandpoonia/1-grid-computing
• 13. http://stackoverflow.com/questions/782913/googles-bigtable-vs-a-relational-database

Uwagi

1. Preface

2. Technical Session: First International Conference on Information Technology and Knowledge Management