Background information

Cloud Technology can be used to store data information and provides a platform for data analytics. The amount of data generated from diverse source is amounting to an amount that cannot be processed by a single processor. The need of big data technology resolves this problem by providing a platform of manipulation of the data which is larger in size.

The main purpose of this report is to provide a brief overview on the different aspect on big data and cloud computing with the emphasis on the Netflix as a provider on each. Analyzing how this technology is used as a source of business intelligence, improving decision making and the opportunities involved in these technologies.

Background and Definitions for Big Data

Big data is a term which refers to the data set that is large or which is complex that traditional data processor is unable to process. The challenges include analysis, capture, data, sharing, and transfer. Data sets grow rapidly because of the increasing gathering of the data from different source (Rittinghouse and Ransome 2016)..

Cloud computing refers in configuring, manipulating and accessing the application data online, which offers a storage, data infrastructure and application in a form of cloud (Yadav and Doke 2016). Mobile Cloud Computing (MCC) is a the internet rich media experience and requires less processing in cloud, data is stored and mobile devices are serve as a media to display. MCC uses five kinds of resources: Distant immobile cloud, distant mobile cloud, proximate mobile computer, Proximate immobile and Hybrid

Mobile digital platform is mainly composed of two parts: a mobile client application and a mobile middleware server. The middleware is responsible for system integration, scalability and cross platform support (Rittinghouse and Ransome 2016). No data is stored in this part it just manages the data which is in the back end system to the mobile device and back. Mobile application is a software that connects to the middleware that drives the both business logic and the user interface (Yadav and Doke 2016). These platform are able to transfer data cross platform seamlessly across the operating system of the mobile

Evaluation of Benefits and Challenges of Big Data

Big Data has emerged within the past years as an ideal provider of data and opportunities to enable research and decision-support application, as we have discussed earlier, Big Data faces certain challenges like:

Data Storage: Data Storage challenges pose the volume, velocity and variety in Big Data. Storing data in earlier days were problematic since traditional storages often fail to store large amount of data (Singh and Chana 2016). In traditional storage system it is difficult to achieve the velocity of Big Data required in storage systems that is to be scaled up as fast as possible. Cloud storage services offers virtual storage which are unlimited and has higher fault tolerance providing potential solution to address the store challenge in Big Data. Transferring and hosting Big Data on the cloud is quiet expensive as compared to other sources (Almorsy, Grundy and Müller 2016).

Specific Analysis relevant to your chosen case study organisation

Data Transmission: Data transmission process has a life cycle which is: (1) sensors to storage data collection; (2) data management that transfers the integrated data to cloud platform; (3) data integration from multiple data centers; (4) data analysis for moving data from storage for analyzing host.

Data Management: The data management paradigm demands for new technologies to clean, store and organize unstructured data. Metadata are effective used to integrate data provenances, but still the challenge remains to automatically generate metadata to describe Big Data and relevant the processes. The veracity and variety of Big Data are used to refine the paradigm in data management.  Big Data itself poses challenges to the Database Management System (DBMS) because the traditional Relational Database Management System (RDBMS) lacks scalability for managing and storing unstructured data. NoSQL databases are hence designed for Big Data such that to handle geospatial algorithms which remain another challenge issue (Rittinghouse and Ransome 2016).

Benefits of using big data

• Fully understanding data driven marketing as a potential.
• With respect to the buying habits generating customer offers
• Quickly reevaluating risk portfolios
• Increasing customer loyalty and improving customer engagement
• Identifying the root cause of the failure and the issue in the real time(Almorsy, Grundy and Müller 2016).

The benefits of cloud computing includes:

1. Usability: This allows users to drag and drop files between their local storage and the cloud storage
2. Bandwidth: this allows the user to send a web link to the recipant through email.
3. Accessibility: it stores file which can be accessed from anywhere.
4. Cost saving: the user can have additional cost saving due to the factor that it does not require internal power (Almorsy, Grundy and Müller 2016).

The Challenges are given below:

1. security and privacy: The main challenge of cloud computing is addressing the security aspect to those businesses thinking about adopting cloud computing
2. Interoperability: Cloud computing services should have the capability to smoothly integrate with on-premises IT
3. Reliability and availability: Businesses can save money with the hardware part but they have to spend money on the bandwidth part (Almorsy, Grundy and Müller 2016).

Benefits

1. Reusable code: IT simplifies the task of main of maintaining  and deploying codes and elevates repetitive task
2. Cost effective: Building a cross platform mobile app can turn out to be economical for any organization.
3. Easy deployment: many of the framework  support variety of modules and extension that seamlessly migrate with the other tools (Dastjerdi and Buyya 2016).

Challenges

1. Vendor /platform fragmentation:  there are three main platforms IOS, Android and windows. Each platform does not support every application as a result of which application respectively for each platform had to be made.
2. Development approach:  there are mainly three development approach native , HTML 5 and hybrid. For each development approach there are different operating system.
3. Device fragmentation: this effects when some uses older versions of the operating system while there are others who use newer version (Dastjerdi and Buyya 2016).

Analysis on the Impact of Big Data as a Source for Business Intelligence

Netflix provide end to end big data solutions which are based on years of innovation (Dave, Patel and Bhatt 2016). The concept enables to capture, process, and store and analyze a video within a single platform (Almorsy, Grundy and Müller 2016). Combining cloud native services with open source tool both in stream and batch mode can be done with Amazon cloud platform  which focuses apart from managing infrastructure it also deals with finding insight. For any organization integrating, normalizing, storing play a challenging role (Dave, Patel and Bhatt 2016). The current business and technical needs are no longer meeting by existing data management technologies.

Business Intelligence coping up with big data was a huge challenge. In the market of competition it’s no longer just how a company went from point A to point B (Di Spaltro et al 2016). The power of big data, and an integrated intelligence and big data analytics perform can help organizations. Business intelligence with respect to big data is a new concept. Many IT professionals including Google have worked up with the concept (Almorsy, Grundy and Müller 2016). This concept is not only about technology its concept ranges to social media, customer segmentation, and customer behavior to name a few. Big data cannot be just plugged into some big application it has to be integrated into one platform and rolled up visually innovation solution (Hinton 2016).Netflix is a company driven by data. The company does need a license from the studios in order to deliver the streaming site (Dave, Patel and Bhatt 2016). The role of intelligence plays a vital role in gaining insight into the customer, taking into account the need of the customer. Analysis gives the business a way of improving the service which can be related to businesses in the data form of quantitative. At current Netflix is having 98.75 users worldwide who stream their video which is estimated to become double in the near future (Almorsy, Grundy and Müller 2016).

The Business Impact of Cloud Computing and Big Data

The amount of data increasing day by day by the users creates a lack of environment that can process that can process the data (Dave, Patel and Bhatt 2016). The tem big data is termed as large data sets. It brings about new opportunity for the new values that are hidden temporarily.

Simon’s decision model summarizes the decision making process in three phases

PROBLEM STRUCTURING METHODS (PSM): PSM are usually used by people in group form. Some of the widely used PSM’s include the strategic choice approach, soft system methodology and Analysis (SODA) (Dave, Patel and Bhatt 2016). PSM’s assume that with respect to user there is no uncontested single representation of what the problem constitutes of.

MULTI- CRITERIA DECISION AID (MCDA): IT is a sub- discipline of research of operation that explicitly evaluates in decision making in conflicting criteria’s (Di Spaltro et al 2016). Conflicting criteria’s can be related to cost and price which are the main factors. On the other hand quality can be typically being other criteria.

KM TECHNIQUE: The main aim of the Technique is to provide a comprehensive overview of the management (Dave, Patel and Bhatt 2016) of the knowledge putting focus on the objectives, best practice, and knowledge management tools and examining its objectives

KM technique involves processes such as communities of practice, brainstorming, yellow pages, peers assistance, narratives, best practices and knowledge mapping (Almorsy, Grundy and Müller 2016).

These methods can be actively aid in the elicitation of knowledge of the actor involved in decision making process, thus contributing to the expertise necessary for solving a particular problem (Almorsy, Grundy and Müller 2016). The main purpose of this work is the integration of the decision making process with the help of the tools involved with it considering the predictive perspective of the approach involved in decision making. In this approach it is very important to make sure that the method to structural decision problem and search alternatives to suggest is done appropriately.

Cloud computing and Big Data is better than traditional database in several ways:

• Information is stored in a global server
• Cloud servers are easy to maintain
• Can be accessed from any device
• Low cost setup (Choo et al. 2017).

Combination of Big Data and Cloud Computing in the organization has made a rapid positive change and has a better impact than Traditional Database system. Adoption of Hadoop has growing rapidly and the ability of performing analytics on non-corrective and affordable hardware has become more everywhere (Almorsy, Grundy and Müller 2016). Netflix Interconnect 2013 has increases value from data judged and gains through big data analytics supported by a cloud infrastructure (Breiter et al. 2014). The benefits of big data and hybrid cloud are harnessed which refers to the explosion in unstructured data.  

Benefits of using Big Data

Data Architecture are built by organizations and storage policies are also being practiced to work with structured data, where as the unstructured data does not fit the RDBMS framework. This results in manipulation and extraction of data essence in case of simply storing and retrieving the data (Sadiku et al. 2014).

Netflix storage solutions provide users the ready data access with speed and performance with the acuteness of hybrid cloud and software-defined storage. It connects the data across any storage and architecture from Netflix (Ni, Xiao and Tan 2016). Delivering the intuitions much faster and hence giving the edge of outthinking. Different types of storage in Netflix are: Flash storage, Software-defined storage, Hybrid storage, Tape storage, Storage area network and converged infrastructure. Netflix collects and troubleshoots the data collection in two ways. If experienced performance issue, there are two ways to collect system data, first is to take a system snapshot and collect summary of the data and performance state, it helps in building the files in approximately 5 minutes and the second option is to allow collection of historical system information for a time period past up to 12 hours. This helps in collecting data soon after the problem is faced (Inukollu et al. 2014).

Conclusion

From the above discussion it can be concluded that Cloud computing and big data are playing a vital role in every aspect of technology. A single processor was not able to access or manipulate the large amount of data at a single time. The burden of processing huge amount of data by a single processor unit is reduced with the use of big data concept .With the cloud computing technology Netflix is gathering the access to large amount of video available or online streaming. This has helped the company to gather more user access at a single instance of time. The hit access to the website has also increased considerable amount. The security aspect of the access was also taken care of with the implementation of the pre defined security features of the cloud computing.  The aspect of fast access to a large collection of video data which can be streamed anytime and from anywhere gives it an added advantage.

Giving lime light on any technology the aspect of security can play a vital role. The access of a large number of users at a single time can make it vulnerable to data thief. Security features need to be updated all the time to keep pace with the technological advancement. The data of the user should be kept at the access of the user only ensuring the security of the data.

Challenges of using Big Data

Another important fact regarding the use of the big data is the identification of the proper data. In order to use the big data analytics efficiently, the users should collect the valid data that can be used for the decision making procedure of the organization.

While using the cloud computing technology the user organization should be aware about the third party handling of the important data. The selection of an trustworthy vendor is very important.

References

Ali, A., Warren, D. and Mathiassen, L., 2017. Cloud-based business services innovation: A risk management model. International Journal of Information Management, 37(6), pp.639-649.

Almorsy, M., Grundy, J. and Müller, I., 2016. An analysis of the cloud computing security problem. arXiv preprint arXiv:1609.01107.

Barreto, L., Celesti, A., Villari, M., Fazio, M. and Puliafito, A., 2016. Security and IoT Cloud Federation: Design of Authentication Schemes. In Internet of Things. IoT Infrastructures: Second International Summit, IoT 360° 2015, Rome, Italy, October 27-29, 2015. Revised Selected Papers, Part I (pp. 337-346). Springer International Publishing.

Choo, K.K.R., Esposito, C. and Castiglione, A., 2017. Evidence and Forensics in the Cloud: Challenges and Future Research Directions. IEEE Cloud Computing, 4(3), pp.14-19. Márquez, F.P.G. and Lev, B. eds., 2017. Big Data Management. Springer International Publishing.

Dastjerdi, A.V. and Buyya, R., 2016. Fog computing: Helping the Internet of Things realize its potential. Computer, 49(8), pp.112-116.

Dave, A., Patel, B. and Bhatt, G., 2016, October. Load balancing in cloud computing using optimization techniques: A study. In Communication and Electronics Systems (ICCES), International Conference on (pp. 1-6). IEEE.

Di Spaltro, D., Polvi, A. and Welliver, L., Rackspace Us, Inc., 2016. Methods and systems for cloud computing management. U.S. Patent 9,501,329.

Flint, D., 2017. Storms Ahead for Cloud Service Providers. Business Law Review, 38(3), pp.125-126.

Gupta, P. and Jha, R.S., 2017. Information Retrieval and Access in Cloud. In Library and Information Services for Bioinformatics Education and Research (pp. 212-228). IGI Global.

Hinton, H., 2016. Security and Compliance: IaaS, PaaS, and Hybrid Cloud. Handbook of Research on End-to-End Cloud Computing Architecture Design, p.159.

Ibrahim, A.S., Hamlyn-Harris, J. and Grundy, J., 2016. Emerging security challenges of cloud virtual infrastructure. arXiv preprint arXiv:1612.09059.

Ni, L.M.S., Xiao, J. and Tan, H., 2016. The golden age for popularizing big data. Science China Information Sciences, 59(10), p.108101.

Park, H., Lee, E.J., Park, D.H., Eun, J.S. and Kim, S.H., 2016, October. PaaS offering for the big data analysis of each individual APC. In Information and Communication Technology Convergence (ICTC), 2016 International Conference on (pp. 30-32). IEEE.

Prasad, M.G. and Sujatha, N., 2016. AN EFFECTIVE APPROACH FOR SUPPORTING CLOUD SERVICE PROVIDER SELECTION. IJITR, 4(4), pp.3079-3081.

Rittinghouse, J.W. and Ransome, J.F., 2016. Cloud computing: implementation, management, and security. CRC press.

Sethi, S. and Sruti, S., 2016. Cloud Security Issues and Challenges. Resource Management and Efficiency in Cloud Computing Environments, p.89.

Singh, S. and Chana, I., 2016. A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing, 14(2), pp.217-264.

Yadav, D.S. and Doke, K., 2016. Mobile Cloud Computing Issues and Solution Framework.

Yan, Q., Yu, F.R., Gong, Q. and Li, J., 2016. Software-defined networking (SDN) and distributed denial of service (DDoS) attacks in cloud computing environments: A survey, some research issues, and challenges. IEEE Communications Surveys & Tutorials, 18(1), pp.602-622.