We explore Apache Spark, the newest tool to  analyze big data, which lets programmers perform in-memory computation on large data sets in a fault tolerant manner. MapReduce is a high-performance distributed BigData programming framework which is highly preferred by most big data analysts and is out there for a long time with a very good documentation. The purpose of this project was to compare the scalability of open-source distributed data management systems like Apache Hadoop for small and medium data sets and to compare it’s performance against the Apache Spark, which is a scalable distributed in-memory data processing engine. To do this comparison some experiments were executed on data sets of size ranging from 5GB to 43GB, on both single machine and on a Hadoop cluster. The results show that the cluster outperforms the computation of a single machine by a huge range. Apache Spark outperforms MapReduce by a dramatic margin, and as the data grows Spark becomes more reliable and fault tolerant. We also got an interesting result that, with the increase of the number of blocks on the Hadoop Distributed File System, also increases the run-time of both the MapReduce and Spark programs and even in this case, Spark performs far more better than MapReduce. This demonstrates Spark as a possible replacement of MapReduce in the near future.

In this paper, the authors have proposed an innovative approach for generating a pseudo random ternary sequence by using a primitive polynomial, trace function, and Legendre symbol over odd characteristics field. Let p be an odd prime number, F_P be an odd characteristic prime field, and m be the degree of the primitive polynomial f(x) Let w be its zero and a primitive element in F_pm* In the beginning, a primitive polynomial f(x) generates maximum length vector sequence, then the trace function Tr(.) is used to map an element of the extension field (F_pm) to an element of the prime field F_p then non-zero scalar A∈F_p is added to the trace value, and finally the Legendre symbol (a/p) is utilized to map the scalars into ternary sequence having the values, {0,1,and -1} By applying the new parameter A the period of the sequence is extended to its maximum value that is n=p^m-1 Hence, our proposed sequence has some parameters such as p,m,and A This paper mathematically explains the properties of the proposed ternary sequence such as period and autocorrelation. Additionally, these properties are also justified based on some experimental results.