Cloud Based Big Data Analytics
Big Data is a term refers to Structured, unstructured and Semi structured data that is this data is having variety. Big Data is also referred a term as a data is a huge data set having really huge magnitude. Volume (really a huge volume). Big data is that term which arrives before you and your organization has had to deal with before i.e. big data have velocity .This flood of data is generated by connected devices from PCs and smart phones to sensors such as RFID readers and traffic cams, In health care, for instance, clinical data can now come in the form of images (e.g. from X-rays, CT-scan, and ultrasound) and videos. Imaging data collected from one patient alone can easily consume several Gigabytes of storage space. Cloud is term referred as using internet as a backbone for utilization of services on remote servers to store manage and process data rather than local servers or personal computers. Cloud computing basically developed as a heterogeneous service environment for providing computing facility to end users and now a day emerging service as IOT (Internet of Things).
The real big data gets its hidden ‘V’ besides volume , velocity and variety when that huge/big data get analyzed for discovered patterns, derived meaning, indicators for decisions, and ultimately the ability to respond to the world with greater intelligence. A term “Big data analytics”, is a set of advanced technologies designed to work with large volumes of heterogeneous data. Resent study shows thatcloud become a prominent technology of migrating all applications and services. Researchers thinking for all sectors data should be migrated on cloud for fast decision processing. This ultimately tends to the cloud computing as IAS (Internet as Service). Huge data set will be extracted and made possible decision on basis of knowledge in big data. Extractions required smart scalable analytics services, programming tools, and applications. Big data analytics uses complex data mining algorithms that required efficient high performance processors. Cloud computing infrastructure is able to provide both computational and data processing applications. This paper is organized in chapters, second chapter is for review of cloud computing and big data analytics method and current scenario, third chapter focus on the prominent methods which will be useful for such kind of data analysis, and fourth chapter is analysis and discussing on what are the current challenges in transforming big data analysis in cloud. Finally last we have focus on the future trends and conclude the statement how we can transfer big data analytics in cloud. Scalable data management is a vision and future for next generation computing. From last decade most of the research has focused on large scale data management and migration of that data in cloud from traditional enterprises. Cloud computing will provide this data for future decision. Cloud computing infrastructure and operations will have its own set of novel challenges, one of the most research oriented topic is security . In this article we will primarily focus on the challenges and opportunities for transforming big data into cloud. To understand the fact we have divided the literature as section II will be focuses on the challenges in front of transformation of big data analytics in cloud and also focuses on the advantages of migration. Section III is about the techniques available in for migration. Section IV is focusing on the analysis and brief discussion about reality of migrating big data into cloud. At last Section V is conclusion for the work migration of big data analytics in cloud computing.
CHALLENGES AND OPPORTUNITIES
Cloud computing is trends to most efficient and prominent platform for service oriented computing in last two decade. This ultimately transformed cloud computing in revolutionary infrastructure refinement, the most popular infrastructure is a Platform as a service (PaaS) and Software as Service (SaaS). Finally with this refinement of paradigm one another paradigm is Infrastructure as Service (IaaS) led down cloud computing to concept where servicers will offer as Elasticity , pay- per- user, low affordable investment low upfront investment ,low time to market, and transfer of risks are some of the major enabling features that make cloud computing a ubiquitous paradigm for deploying
novel applications which were not economically feasible in a traditional enterprise infrastructure settings.Whereas another trend which leads the next generation computing is progress in business intelligence and analytics of data. Field of big data analytics emergence from these trends where opportunities for BI (Business intelligence) software arise. Big data researchers classify system as one for supporting update heavy applications, and another is for ad-hoc analytics and decision support. Scalable and distributed data management is a visionary paradigm in transforming big data analytics into cloud .initially researchers developed a distributed database and for maintaining the workload parallel database system both were successful. Change in data access is major issue in front of distributed and parallel data, to resolve that a new class of system defines i.e. Key Value. MapReduce paradigm and its open source implementation platform Hadoopis basically is a solution of this problem of distributed and parallel data bases. The next step is to develop application which works on cloud and manages these big data performed analytics for business intelligence this opens a new possibility. Cloud itself has some features which will support for development of such a complex system which can access data from many sources. These cloud features include scalability, elasticity, fault-tolerance, self-manageability, and ability to run on commodity hardware. Utilization of the features for development of application need a system which can update heavy workloads as internet generates huge amount of data. Here a we focus on discussion on new generation Key-Value data store which has extreme success and adopted by industry i.e. HadoopMapReduce in next section. Here we can say that there are some challenges in big data migration in cloudnamely (i) Scalable Data Management (ii) Data Management for Large Applications (iii) Large Multitenant Databases (iv) security issues for cloud computing, Big data, Map Reduce and Hadoop environment. In next section we are focussing on how these problem get resolve with the current edge technology namely Hadoop and MapReduce.