Transforming Raw Data
Advanced multimedia devices, social media services, sensor networks, and corporate information systems create continuously huge amounts of structured and unstructured data which are called big data. Transforming collected masses of raw data into meaningful and useful information is important for organizations. This knowledge helps managers to make smarter decisions and improve organization’s performance.
The four dimensions of big data (volume, velocity, variety and value) present challenges to businesses such as how to store and manage data, how to effectively analyse data and gain value from big data. Recently, cloud computing has been recognized as a useful technology in handling big data for many of the organizations. “ Cloud Computing platforms provide easy access to a company’s high-performance computing and storage infrastructure through web services”. The driving forces behind cloud computing are: lower infrastructure and software costs, reliability, availability, compatibility, scalability, elasticity, risk reduction, high performance and specifiable configurability. These features make cloud computing a ubiquitous paradigm for deploying novel applications which were not economically feasible in a traditional enterprise infrastructure setting.
The cloud computing model consists of three delivery models which are Software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). In Saas, users can use services to run software on the provider’s infrastructure; Paas provides organizations a platform to use custom applications to analyse large amounts of data at a low cost and low risk in a secure environment. In the IaaS model, organizations can use services such as compute as a service, storage as a service, and virtual desktop infrastructure. Cloud deployment models include public, private and hybrid clouds. The type of cloud a company uses depends on the company’s needs and resources. A cloud environment that is available to the public is called a public cloud which is not secure. In the private cloud, all services and resources are provided based on the needs of the organization, and the organization has total control over the services and resources. Hybrid cloud is a composition of private and public cloud. Organizations can improve their efficiency by employing public cloud services for all non-sensitive operations. A private cloud can be used for resources and services that need to be secure.
One of the primary uses of cloud computing is data storage. With cloud storage, data is stored on multiple third-party servers, rather than on the dedicated servers used in traditional networked data storage. The traditional storage solutions have typically been direct attached storage (DAS) [Storage, disk or tape, is directly attached by a cable to the computer processor] and Storage Area Network (SAN) [Storage resides on a dedicated network]. With the proliferation of local area networks, the use of clustered Network Attached Storage (NAS) has increased. Although clustered network attached storage provide easy access to data while maintaining high performance, easy management, and maximum scalability, clustered NAS storage is an expensive prospect for a small to medium size business. Hence, an increasing number of companies and organizations move their data to cloud storage providers.
Processing of big datasets in an efficient way is a clear need for many organizations. Hadoop MapReduce is one of the popular big data processing models and it is the key to achieve better scalability and performance for processing big data. Ease-of-use, scalability, and failover are important properties of Hadoop Map Reduce. One of the main advantages of Hadoop MapReduce is that non-expert users can easily run analytical tasks over big data. Users can control on how input datasets are processed. Users code their queries using Java rather than SQL. So, it is easy to use for a larger number of developers.
In sum, cloud computing can be considered as an attractive technology platform for developing and deploying big data analysis. The key value from big data comes not from the raw data but from the processing and analysis of it and the insights, products and services that emerge from analysis.
By Mojgan Afshari
Mojgan Afshari is a senior lecturer in the Department of Educational Management, Planning and Policy at the University of Malaya. She earned a Bachelor of Science in Industrial Applied Chemistry from Tehran, Iran. Then, she completed her Master’s degree in Educational Administration. After living in Malaysia for a few years, she pursued her PhD in Educational Administration with a focus on ICT use in education from the University Putra Malaysia.She currently teaches courses in managing change and creativity and statistics in education at the graduate level. Her research areas include teaching and learning with ICT, school technology leadership, Educational leadership, and creativity. She is a member of several professional associations and editor of the Journal of Education. She has written or co-authored articles in the following journals: Journal of Technology, Pedagogy and Education, The Turkish Online Journal of Educational Technology, International Journal of Education and Information Technologies, International Journal of Instruction, International Journal of Learning, European Journal of Social Sciences, Asia Pacific Journal of Cancer Prevention, Life Science Journal, Australian Journal of Basic and Applied Sciences, Scientific Research and Essays.