Big Data can be referred to as the process which can be used when all the traditional methods for data mining and processing have become inadequate in keeping track with the massive generation of data. With so many business ventures, the volume of generated data increased by multitudinous times in the last few years. Thus, data migration problems have come up. Huge volumes of data have become unmanageable by conventional database systems (RDBMS). This is where we need Big Data.
Big Data can be used in many organizations where huge data needs to be processed. Big Data with the cloud is the ultimate solution to accessing, storing and analyzing large volumes of data in a swift and reliable manner.
Relational database engines can only accept data to a limit .There are some unstructured, often time sensitive data which could not be processed by using the relational databases. Thus the unused portion of data from gigantic data chunks can easily be processed and put to use with the help of big data. This ensures maximum utilization of the available data.
Big data is unlocking opportunities for business to flourish, develop and excel. The advent of Big Data has paved way for businesses to take more accurate decisions by smart and detailed data utilization in varied disciplines.
Hadoop is an open source technology which can be used to implement bigdata in a private server of any organization or in the cloud, Hadoop can effectively manage massive chunks of data with its high speeds and stupendous scalability. It is a cost effective way of managing large volume of data. Hadoop can be easily be configured in cloud like AWS. AWS provides the needed infrastructure and configuration facility to implement Bigdata. With Hadoop, larger data chunks are broken down into smaller pieces of work that can be distributed across nodes in the Amazon EMR cluster. Due to the presence of numerous Availability zones, the user can easily refrain from launching a cluster in a zone with potential threats. This helps in better disaster management. Hadoop, along with Amazon EMR, also ensures that data is processed in a seamless manner without much of administrative complexities.
Elastic Search is a mechanism by which we can store, access and process large volume of data. Unlike Hadoop, Elastic Search can installed and configured on a centralized server rather than a distributed system. The popular Elastic Search product is Solr by Apache. This is not a conventional RDBMS approach of storing data. We have vast experience in handling large volume of data. Based on the need of the business, we suggest the technology to optimize cost.
Amazon Kinesis data stream enables user to build any custom application for processing or analyzing streaming data in real time for specific needs. The main advantage of using Kinesis data is that it can store and capture terabytes of data continuously per hour from thousands of sources such as financial transactions, IT logs, and many occasions where data need to be accessed and processed in real time. AWS provides a solution called Kinesis for this specific realtime streaming need. A predefined Kinesis Client Library (KCL), helps in building real time streaming application with large volume of data. It can collect data continuously from thousands of other sources and process them simultaneously.
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