Abstract—Google, Amazon, Facebook and Twitter gained enormous advantages from big data methodologies and techniques. There are certain unanswered questions regarding the process of big data, however, not much research has been undertaken in this area yet. This review will perform a comparative analysis based on big data techniques obtained from sixteen peer-reviewed scientific publications (2007-2015) about social media companies such as Google, Amazon, Facebook and Twitter to undertake a comparative analysis. Google has invented many techniques by using big data methods to strategize against competitors. Google, Facebook, Amazon and Twitter are partially similar companies that use big data despite their own business model requirements. As an illustration, Google required the data “ware housing” approach to store trillion of data related to Facebook, since Facebook owns more than one billion users and Twitter owns 300 million active users correspondingly equally to Amazon. Since all these organization required data ware house approach, Google has preferred the variation of data ware house storages (Spanner, Photon, Fusion table) variation of data transaction methods. By using these data ware house storage approaches (F1 for execute queries via SQL) and communication of different approached such as, Yedalog. Facebook and Twitter are both the only social media companies that have different requirements. The requirement of big data is high and these entire requirements partially depend on each another as it is completely isolated. This study is a useful reference for many researchers to identify the differences of big data approaches and technological analysis in comparison to Google, Facebook, Twitter and Amazon big data techniques and outline their, variations and similarities analysis.
 

Index Terms—Big data, big data techniques, amazon, social networks

Cite: Thulara N. Hewage, Malka N. Halgamuge, Ali Syed, and Gullu Ekici, "Review: Big Data Techniques of Google, Amazon, Facebook and Twitter," Journal of Communications, vol. 13, no. 2, pp. 94-100, 2018. Doi: 10.12720/jcm.13.2.94-100.