BIG DATA AND CLOUD TECHNOLOGY
Organizational change from analog to digital technologies as well as a software upgrade in addition to hi-tech devices has driven the market for data and large data analytics to a new frontier. Big data is the expression not only given to the tremendous amount of data and huge dataset but, the overall technology which deals which such massive amount of data, whereas big data analytics is the process of analyzing large datasets to expose hidden patterns, understand customer preferences, explore unknown correlations and other useful information. In highly regulated industries like finance, healthcare, insurance, retail, and social security, combating fraud is essential, as there are a sizable amount of compliance, regulations, risk management measures, and financial consequences to deal with. Hence enterprises in the government and private sectors are deploying big data solutions that are to create new insights, and to handle the data generated.
Now, economies over the world are using big data analytics as a new frontier for businesses to increase productivity, enhance performance, and plan strategy more efficiently. To make data analytics powerful, tools and storage technologies play a critical role. However, it’s evident that big data creates overbearing demands on networks, storage, and servers, which has prompted organizations and enterprises to proceed to the cloud, to be able to gain maximum benefits of big data.
Cloud technology is popular in medium and small businesses since it provides cost-effective and lower-risk solutions. The elasticity of the cloud makes it perfect for the analysis of structured and unstructured big data. To be able to decrease the expense of storage, cloud technology allows real-time analytics to explore and exploit large datasets. At Present, the trend in the industry worldwide is to perform big data analytics from the cloud, as the duo provides agility, affordability, scalability, and many more benefits.
BIG DATA: DEFINITION, OPPORTUNITIES, AND CHALLENGES
There has been lots of brainstorming in academia and industry regarding the definition of big data. However, to reach a consensus is slightly difficult. The term big data is defined in various ways by considering several dimensions regarding its overall process. Among the apt definitions of big data is the one described by the five V’s, i.e., Variety, Volume, Velocity, Veracity, and Value. Big data volume indicates the size of the data, which is the quantity of data collected over the last couple of decades and is being recorded every moment. This makes it possible to develop gain far-reaching insights and no data is considered as insignificant and therefore all are stored for future references. The variety of data indicates its various forms like unstructured and structured. Any information in which there’s a well-formed set of rules formed is known as structured data such as a database containing particulars of employees,ultra-sonography reports, blogs, audio, video. Data is termed as unstructured when there’s a lack of this type of principle data such as comments, tweets, and feedback given for internet shopping. The velocity of data is applicable to streaming data. The frequency with which the information is created at every tick of the clock from SMS from friends, social media articles, card swipes, email updates, financial transactions, promotional messages, etc., into the machine/sensor-generated data, Everything adds to the velocity of data. The value of big data denotes the costs and benefits of aggregating and analyzing the data to ensure that ultimately it can be monetized. Veracity refers to data in doubt and indicates the need for trustworthiness and reliability. The data is meant to be free from all discrepancies like incompleteness, data redundancy, outliers, and inconsistency. These five V’s are considered as the pillars of data that is big.
When the data grows big enough in all viewpoints like volume, velocity, variety, value, and veracity so that it becomes difficult to process it using traditional approaches, then it’s certainly a big data. Big data is generated while the amount of the data increases from terabytes to zettabytes, speed increases from batch data to streaming data, when amount, value, and complexity rises from structured data to unstructured data and when present database management tools become incapable to record, modify, query, and analyze. Big data analytics uses analytic techniques that help companies to take agile and quick decisions based on a 360 ° perspective of its processes and their company.
Additionally, if companies improve the quality of data, its usability, and implement the intelligence gained, it can easily generate profits as well as possess the cutting edge over competitors. This can pose both challenges and opportunities. The opportunities lie in unveiling the benefits in various sectors of businesses and choosing it as a career of technology professionals who will explore and exploit the power of big data. And the challenges are due to the growth of data that puts a high demand on storage systems and infrastructures, which are incompatible processes in terms of software and hardware required to assimilate big data.
THE FUSION OF BIG DATA WITH CLOUD TECHNOLOGY
When big data computing takes place in the clouds it is known as “Big Data Clouds”. Their purpose is to build an integrated infrastructure that is suitable for quick analytics and deployment of an elastically scalable infrastructure. Cloud technology is used to derive quantum-leap advantages inherent in big data.
The features of big data clouds are as follows:
Large-scale Distributed Computing: A wide range of computing facilities intended for distributed architectures.
Data Storage: These are all the scalable storage facilities and data services that work seamlessly.
Metadata-based Data Access: The insight generated from the data is used instead of using path and filenames.
Distributed Virtual File System: Files are arranged in a hierarchical structure, where the nodes are the directories.
High-performance Data and Computation: The data, as well as computations, is driven and enhanced by performance.
Analytics Service Provider: It helps to develop, deploy, and use analytics.
Multi-dimension Data: It provides the needed support for various data tools and types to facilitate their processing.
Analytics Service Provider: It enables the development, deployment, and use of analytics.
High Availability of Data and Computing: Duplication mechanism of both data and computation is carried out to make them available always.
Integrated Platform: It gives the needed platform for fast analytics and deployment of elastically scalable architecture waiting on the services offered to the end-user.
The clouds are classified into four different types on the basis of their usage:
Public Big Data Cloud: In this cloud, resources are offered as a pay-as-go computing model and stretch over a large-scale organization. This cloud is scalable elastically in terms of architecture. For example Google cloud platform of big data computing, Windows Azure HDInsight, Amazon big data computing in clouds, RackSpace Cloudera Hadoop
Private Big Data Cloud: This is the cloud within an organization with the aim of providing privacy and greater resource control through virtualized infrastructure.
Hybrid Big Data Cloud: This cloud espouses the characteristics and functionalities of private and public clouds. Its purpose is to attain network configuration and latency, scalability, high availability, data sovereignty, disaster recovery, and compliance and workload portability. During the period of heavy workloads, its deployment facilitates the migration of private workloads to public architecture.
Big Data Access Networks and Computing Platform: This is an integrated platform designed for data, analytics, and computing. This platform provides services by multiple distinct providers.
The fusion of big data and cloud technology gives rise to many elements and services discussed below:
Cloud Mounting Services or Mount Cloud Storage: It is a system designed to mount data to many web servers and cloud storage. For example Amazon, Google drive.
Big Data Cloud: It is an infrastructure designed to manage different data sources like data management, data access, security, schedulers, programming models, and so on. The big data cloud infrastructure has many tools like streaming, web services, and APIs for collecting data from different sources such as Google data, social networking, relational data stores, and so on.
Cloud Streaming: It is the act of transferring multimedia data such as video and audio in a way that makes them readily available in continuous mode over social media infrastructure.
Social Media: These are platforms that enable the gathering of multitudes online. It is the major source of big data utilizing the services of cloud technology. Examples are Facebook, LinkedIn, Twitter, and YouTube.
HTTP, REST Services: These services are designed to develop APIs for web-based applications that are lightweight, scalable and maintainable. For example USGS Geo Cloud, Google Earth.
Big Data Computing Platform: It is designed to create modules required to manage different data sources like Data-intensive programming models, Data security, Compute and Data-aware scheduling, Appliances, Distributed file system, and Analytics.
Computing Cloud: This platform is developed to give the required computing infrastructure like physical and virtual compute as well as storage from private, public, and hybrid clouds.
Big Data Cloud Reference Architecture
The cloud architecture for big data is efficient to manage complicated computing scalability, storage, and networking infrastructure. The infrastructure as service providers mainly deals with servers, networks, in addition to storage applications and offers facilities such as virtualization, basic monitoring and safety, operating system, server in a data center, and storage services. The four layers of large data cloud architecture are discussed below:
Big Data Analytics-Software as a Service (BDA-SaaS ): The analytics of big data offered as service gives users the capability to quickly work on analytics without spending on infrastructure and pay for the facilities used. The functions of this layer are:
• Arrangement of software applications repository
• Software programs deployment on the infrastructure
• Result delivery to the users.
Big Data Analytics-Platform as a Service (BPaaS ): This is the second layer of the architecture. It is the core layer that provides platform-related services to work with stored big data and computing. Data management tools, schedulers, and programming environments for data-intensive and data processing tasks, which are considered as middleware management tools reside in this region. This layer is responsible for developing software development kits and tools necessary for analytics.
Big Data Fabric (BDF): This is the fabric layer of big data, responsible for addressing tools and APIs that support the storage of data, data computation, and access to different application services. This layer comprises APIs and interoperable protocol designed to connect the specified multiple cloud infrastructural standards.
Cloud Infrastructure (CI): The cloud infrastructure is responsible for handling the infrastructure for data storage and computation as services. The services offered by CI layer are as follows:
• To create large-scale elastic infrastructure for big data storage, capable of on-demand deployment.
• To set up dynamic virtual machines.
•To generates on-demand storage facilities that relate to big data management for file, block, and object-based.
• To enable seamless passage of data across the storage repositories.
• To create virtual machines and to mount the file system with the compute node.
Final Thoughts on the relationship between Big Data and Cloud Computing
The deployment of cloud technology will make it possible for organizations to merge supplementary infrastructural technologies such as software-defined parameters to create platforms that are dynamic robust and secure. Currently, companies like Google, Microsoft, and Oracle, are providing cloud services. Cloud technology has revolutionized the traditional model of service provisioning, allowing delivery of big data over the internet of virtualized services that can scale up and down in terms of processing power and storage. One of the inherent benefits of deploying services on the cloud is the economy of scale. By utilizing the cloud infrastructure, a service provider can provide cheaper, more efficient, and more reliable services.
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