Cloud computing deals with heterogeneity and dynamicity at all levels and therefore there is a need to manage resources in such an environment and properly allocate them. Resource planning and scheduling requires a proper understanding of arrival patterns and scheduling of resources. Study of workloads can aid in proper understanding of their associated environment. Google has released its latest version of cluster trace, trace version 2.1 in November 2014.The trace consists of cell information of about 29 days spanning across 700k jobs. This paper deals with statistical analysis of this cluster trace. Since the size of trace is very large, Hive which is a Hadoop distributed file system (HDFS) based platform for querying and analysis of Big data, has been used. Hive was accessed through its Beeswax interface. The data was imported into HDFS through HCatalog. Apart from Hive, Pig which is a scripting language and provides abstraction on top of Hadoop was used. To the best of our knowledge the analytical method adopted by us is novel and has helped in gaining several useful insights. Clustering of jobs and arrival time has been done in this paper using K-means++ clustering followed by analysis of distribution of arrival time of jobs which revealed weibull distribution while resource usage was close to zip-f like distribution and process runtimes revealed heavy tailed distribution.