Emerging Trends in Big Data Analytics—A Study

Abstract

Big data refers to exceptionally large datasets that are growing exponentially with time. The three key enablers for the growth of big data are (1) data storage, (2) computation capacity, and (3) data availability (Grobelnik M, Big-Data tutorial, 2012 [1]). This massive, heterogeneous, and unstructured digital content cannot be processed by traditional data management techniques and tools effectively, but this problem is overcome by using big data analytics. In this paper, we have discussed various big data services, languages, and data visualization tools. Big data helps organizations to increase sales and improves marketing results. It also improves customer service, reduces risk, and improves security. Both high storage and computation are important requirements for big data analytics. Information technology researchers and practitioners have faced the major challenge of designing systems for the efficient handling of data and its analysis for the decision-making process as the amount of data continues to grow. Big data is available in three forms, namely structured, unstructured, and semi-structured. The top ten big data technologies are (1) predictive analytics, (2) NoSQL databases, knowledge discovery and searching, (4) stream analytics, (5) data fabric for in memory computing, (6) distributed file stores, (7) virtualization of data, (8) integration of data, (9) preparation of data, and (10) quality of data. Amazon Elastic MapReduce, Apache Hive, Apache Pig, Apache Spark, MapReduce, Couchbase, Hadoop, and MongoDB are data integration tools used to manipulate big data accurately.