Emergence of NoSQL Platforms for Big Data Needs

Big data is revolutionizing world in the age of Internet. The wide variety of areas like online businesses, electronic health management, social networking, demographics, geographic information systems, online education etc. are gaining insight from big data principles. Big data is comprised of heterogeneous datasets which are too large to be handled by traditional relational database systems. An important reason for explosion of interest in big data is that it has become cheap to store volumes of data and there is a major rise in computation capacity. To extract valuable patterns from big data, one needs to choose a right platform for capturing, organizing, searching and analyzing the context of voluminous data in combination with traditional enterprise database management systems. Different platforms supporting big data management by many software organizations enable easy use of services. These platforms mainly focus on data storage, management, processing, and distribution and on data analytics. Various NoSQL data stores like Cassandra, MongoDB and Hadoop HBASE etc. are in use today to acquire, manage, store and query big data. NoSQL databases are inherently schema-less and permit records to have variable number of fields, making them distinct from other non-relational databases like hierarchical databases and object-oriented databases. These are highly scalable and well suited for dynamic data structures. NoSQL data is characterized by being basically available and eventually consistent. The frameworks like MapReduce, Dryad etc. support processing of large amounts of data in parallel and hence the management and analysis of big data. The technologies like GNU R and Apache MAHOUT are also useful in exploring big data for finding relevant valuable patterns. This article aims at giving an overview of the rationales behind NoSQL movement as well as various big data platforms useful in today’s competitive world.