Handling Non-Relational Databases on Big Query with Scheduling Approach and Performance Analysis

Non-relational database computing in Clouds is a new model for upcoming generation analytics development, enabling unstructured data organization, sharing, and exploration of large volumes rapidly growing variety forms of data using Cloud computing technologies as a back end large scale service oriented computational infrastructure facility. Advances in information technology and its extensive growth in numerous areas of business, engineering, medical and scientific studies are resulting in information and data explosion. There are many techniques to handle non-relational database on cloud. This thesis focus on handling non-relational database using scheduling approach. Hadoop is an open source framework that is used to process large amounts of data in an inexpensive and efficient way, and job scheduling is a key factor for achieving high performance in big data processing. This paper uses Google cloud services like big query. Proposed scheduling algorithm was applied on interactive and batch query using cached data and without using cached data. Proposed algorithm reduces waiting time and thus improves the query execution time.