Social network is defined as the interconnection of number of social entities with a variety of relationships. Usually social entities in a social network are of similar types. However, they may be heterogeneous. They can be interdependent through various relationships like financial transaction, message exchange, friendship, common interest, sexual relationships, common research ideas etc. The social network probably is the largest source of data deluge in the world of big data. Analysing large scale data and extracting useful information in the social network is one of the most challenging task of analysts. Social network analysis may involve content or structural analysis of the network. Social media data is increasing at an exponential rate. Traditional processing systems like relational database system, SQL, centralized processing units are unable to analyse such enormous amount of data. Distributed platform, where data can be distributed across multiple computing nodes can be adopted in analysing and extracting the useful information from the network. In this chapter, a different aspect of social network analysis along with their applications have been presented. This chapter focuses on structural analysis of social network rather than content analysis. It also discusses the impact of big data on the social network. Hadoop and Spark are found to be most suitable frameworks for big data analysis in social network. Hadoop is basically used for batch processing in cluster of nodes whereas Spark is suitable for streaming processing for real time data. Comparative analysis Hadoop and Spark performances have also been presented in this chapter.