In the contemporary world, Data analysis is a challenge in the era of varied inters- disciplines though there is a specialization in the respective disciplines. In other words, effective data analytics helps in analyzing the data of any business system. But it is the big data which helps and axialrates the process of analysis of data paving way for a success of any business intelligence system. With the expansion of the industry, the data of the industry also expands. Then, it is increasingly difficult to handle huge amount of data that gets generated no matter what's the business is like, range of fields from social media to finance, flight data, environment and health. Big Data can be used to assess risk in the insurance industry and to track reactions to products in real time. Big Data is also used to monitor things as diverse as wave movements, flight data, traffic data, financial transactions, health and crime. The challenge of Big Data is how to use it to create something that is value to the user. How can it be gathered, stored, processed and analyzed it to turn the raw data information to support decision making. In this paper Big Data is depicted in a form of case study for Airline data based on hive tools.

1. Challenges and opportunities with Big Datahttp://cra. org/ccc/wpcontent/uploads/sites/2/2015/05/bigdatawhitepaper. pdf
2. Oracle: Big Data for Enterprise, June 201http://www. oracle. com/us/products/database/big-data-for-enterprise-519135. pdf
3. Marta C. González, César A. Hidalgo, and Albert-László Barabási. 5 June 2008 Understanding individual human mobility patterns. Nature 453, 779-782.
4. James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers. May 2011 Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
5. Yuki Noguchi. Nov. 30, 2011 The Search for Analysts to Make Sense of Big Data. . National Public Radio. http://www. npr. org/2011/11/30/142893065/thesearch-for-analysts-to-make-sense-of-big-data
6. Data set is taken from edureka http://www. edureka. co/my-course/big-data-and-hadoop
7. Manjunath T N et. al, Automated Data Validation for Data Migration Security, International Journal of Computer Applications (0975 – 8887), Volume 30– No. 6, September 2011. (Imp act Factor=0. 88)
8. Manjunath T N et. al, The Descriptive Study of Knowledge Discovery from Web Usage Mining, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 1, September 2011 ISSN
9. HiveQL Language Manual
10. Apache Tez
11. Working with Students to Improve Indexing in Apache Hive
12. Baru C. K. , Fecteau G. , Goyal A. , Hsiao H. , Jhingran A. , Padmanabhan S. , Copeland, To appear in OSDI 2006 13 G. P. , and Wilson W. G. DB2 parallel edition. IBM Systems Journal 34, 2 (1995), 292. 322.
13. ORACLE. COM. www. oracle. com/technology/products/-database/clustering/index. html.
14. Ratnasamy S. , Francis P. , Handley M. , Karp R. , and Shenker S. A scalable content-addressable network. In Proc. of SIGCOMM (Aug. 2001), pp. 161. 172.
15. Rowstron A. , and Druschel P. Pastry: Scalable, distributed object location and routing for largescale peer-to-peer systems. In Proc. of Middleware 2001 (Nov. 2001), pp. 329. 350.
16. Stoica I. , Morris R. , Karger D. , Kaashoek, M. F. , and Balakrishnan H. Chord: A scalable peer-to-peer lookup service for Internet applications. In Proc. of SIGCOMM (Aug. 2001), pp. 149. 160.
17. Stonebraker M. The case for shared nothing. Database Engineering Bulletin 9, 1 (Mar. 1986), 4. 9.
18. Zhao B. Y. , Kubiatowicz J. , and Joseph A. D. Tapestry: An infrastructure for fault-tolerant wide-area location and routing. Tech. Rep. UCB/CSD-01-1141, CS Division, UC Berkeley, Apr. 2001.

Airline Data Set Hive Tools.