DAT 260 Project One_

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1 Feasibility Proposal Created by February 4, 2024

2 Cloud Computing Cloud computing comprises five deployment models: public, private, hybrid, multi- cloud, and community cloud. Public cloud is ideal for organizations seeking elastic scalability and resource efficiency, particularly to handle intermittent high loads (LaunchDarkly, 2022). Private cloud is recommended for handling sensitive data, offering two options: internal management or partnering with a cloud provider for resource isolation (LaunchDarkly, 2022). Hybrid cloud is suitable when security or data protection requirements necessitate a combination of on-premises and public cloud deployment for mission-critical applications (LaunchDarkly, 2022). Multi-cloud is advantageous for organizations seeking redundancy or cost optimization, commonly used when specific services are required from different cloud providers (LaunchDarkly, 2022). Community cloud caters to a subset of organizations within the same community, tailored to their specific requirements (LaunchDarkly, 2022). In addition to deployment models, cloud computing includes three service models. Infrastructure as a Service (IaaS) is optimal for organizations desiring full control over infrastructure, operating systems, and runtime, with scalable and customizable resources, including virtual machines, storage, and network components. Platform as a Service (PaaS) is recommended for application development and deployment, focusing on rapid development, scalability, and easy maintenance. Software as a Service (SaaS) is well-suited for accessing consistent and up-to-date information while minimizing the management burden of software updates and maintenance. Benefits and Drawbacks of the Cloud Utilizing cloud deployment models comes with both advantages and disadvantages. Among the benefits are cost efficiency, eliminating the need for substantial upfront expenses in

3 hardware, maintenance, and physical infrastructure. This cost reduction enables organizations to redirect savings toward critical initiatives. Improved collaboration is another positive aspect, enhancing processes, data accessibility, and real-time updates among professionals. The scalability of cloud services ensures optimal performance during peak times, offering on-demand scalability. Additionally, cloud deployment models contribute to data security by providing additional checkpoints to secure housed data, although complete risk elimination is not guaranteed (Harms, 2023). Conversely, drawbacks of cloud deployment models include the necessity for training and expertise. Transitioning requires training programs to equip individuals with the necessary skills to effectively use the tools and platforms. Integration may prove complex, demanding significant time and resources to ensure seamless interoperability. Internet dependence becomes a drawback in areas with unreliable networks or susceptibility to power outages. Privacy and compliance issues may also arise depending on the implemented deployment model (Harms, 2023). In comparison, on-premise models boast benefits such as heightened security, privacy, and compliance, as the organization controls the hardware and physical infrastructure, allowing for greater control and implementation of security measures. Additionally, during internet outages, as long as employees are directly connected to the network, there is no lapse in access. Drawbacks of on-premise models include upfront costs for acquiring hardware, infrastructure, and dedicated staff needed for data housing and maintenance, in addition to potential scalability and maintenance expenses. Wait times for implementations and upgrades may also disrupt day- to-day operations (Harms, 2023).

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4 Cloud Deployment Models Various risks and benefits are associated with different cloud computing deployment models, contingent on the specific needs of the business. Public clouds offer advantages such as upfront cost savings, scalability, and the capability to handle intermittent high loads. However, risks include potential exposure of sensitive data, non-compliance with certain industries, and the cost factor: "Pay for everything you use, almost nothing is free. Your datacenter cost is less variable and more fixed" (LaunchDarkly, 2022). Private clouds provide heightened security, data privacy, and isolated resources, but they come with risks such as higher initial costs and a more restrictive catalog of service offerings within the private cloud (LaunchDarkly, 2022). Hybrid clouds offer the flexibility to run sensitive data on-premise while leveraging the benefits of a public cloud, along with reduced migration costs. Risks encompass on-site scalability, unreliable internet connections, and challenges in infrastructure management (LaunchDarkly, 2022). The main advantage of a multi-cloud model lies in the ability to choose the best cloud provider based on business needs. Risks associated with this model include cost considerations, deployment strategies for multiple cloud service providers, and potential troubleshooting system issues (LaunchDarkly, 2022). Finally, community clouds provide benefits similar to private clouds without owning the infrastructure, resulting in cost savings. Risks in this model mirror those of the public cloud but on a smaller scale (LaunchDarkly, 2022). Considerations of Cloud Computing When embracing cloud technology, various factors must be taken into account to guarantee optimal performance, security, and efficiency. Users need to assess the most suitable

5 cloud deployment model for their organization and industry requirements, including options like public, private, community, hybrid, and multi-cloud. Another critical aspect is data security and compliance, especially in industries like healthcare, which have strict regulations governing the storage of sensitive information such as PHI and PII. Users must also evaluate the level of support and maintenance required, which can vary based on the chosen cloud deployment model, potentially necessitating more or less in-house management. As with any tool aimed at enhancing organizational efficiency, challenges are expected. Cost considerations pose a significant challenge, closely tied to the chosen deployment model. Another potential hurdle users may encounter is the integration of the cloud solution with existing systems and applications, requiring a careful assessment of how seamlessly the deployment model can be integrated into the current infrastructure. Big Data vs. Structured Data Traditional data, typically generated within the confines of an enterprise, operates within manageable volumes, measured in gigabytes to terabytes. On the other hand, big data transcends these boundaries, emerging from external sources at an unprecedented scale, ranging from petabytes to zettabytes or exabytes (GeeksforGeeks, 2023). This sheer magnitude emphasizes the challenges and opportunities associated with big data, necessitating a shift in mindset and tools. Moreover, while traditional data resides in centralized databases and adheres to structured formats, big data introduces a paradigm shift by encompassing structured, semi-structured, database, and unstructured data. The frequency of data generation is another critical factor, with traditional data being produced per hour or day, while big data is generated in near real-time, often per second (GeeksforGeeks, 2023). This distinction highlights the need for stakeholders to

6 recognize the dynamic nature of big data and adapt their strategies accordingly, embracing the diversity of data types and sources. In the realm of preprocessing, stakeholders must appreciate the nuanced challenges posed by big data compared to the relatively straightforward nature of traditional data. Traditional data enjoys the luxury of easy data integration, owing to its smaller and stable volume. Processing traditional data requires lower system configurations, and the straightforward nature of the data allows for simpler management and manipulation (GeeksforGeeks, 2023). Contrastingly, big data introduces complexities that demand specialized approaches. Data integration becomes a daunting task due to the diverse nature of structured, semi-structured, and unstructured data. High system configurations are imperative for processing the colossal volumes of big data efficiently. The dynamic and unmanageable nature of big data necessitates the use of specialized tools and functions for manipulation, moving away from the simplicity of normal functions associated with traditional data (GeeksforGeeks, 2023). Stakeholders need to recognize these disparities to invest in the right technologies and strategies for effective preprocessing in the era of big data. Volume, Variety, and Velocity of Big Data The three Vs of big data - volume, velocity, and variety - significantly influence the methods employed for data analysis. Volume, representing the vast amount of data generated, necessitates advanced storage and processing capabilities. Traditional data management tools struggle to handle the immense volumes of big data efficiently (GeeksforGeeks, 2023). The emergence of technologies like Hadoop, Spark, and NoSQL databases reflects the need for specialized tools designed to store, manage, and analyze large datasets in the realm of big data.

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7 Velocity, indicating the speed at which data is generated and processed, adds another layer of complexity. Traditional data, often static and updated periodically, contrasts sharply with the dynamic nature of big data, which is updated in real-time or near real-time. The swift pace of data generation demands agile and real-time analytics capabilities, shaping the methods used to extract insights from big data sources (GeeksforGeeks, 2023). Variety, the diversity of data types and formats in big data, poses challenges to conventional data analysis methods. Traditional data is typically structured, organized neatly in predefined tables, columns, and rows. In contrast, big data can be structured, unstructured, or semi-structured, encompassing text, images, videos, and more. This variety demands versatile analysis methods capable of handling diverse data formats, a departure from the structured nature of traditional data (GeeksforGeeks, 2023). The scale of a big data set fundamentally affects its ability to be processed by conventional methods. Traditional data processing technologies are optimized for small to medium-sized datasets, making them inadequate for the sheer volume and complexity of big data. Conventional tools struggle to efficiently manage and process the large datasets characteristic of big data, necessitating the adoption of advanced technologies like Hadoop and Spark (GeeksforGeeks, 2023). The scale of a data set also influences its usability. Traditional data, being smaller and more structured, is relatively straightforward to manage and analyze. In contrast, the vastness and complexity of big data make it challenging to derive meaningful insights without specialized tools and techniques. The usability of big data hinges on the adoption of technologies designed to handle its scale, ensuring efficient storage, processing, and analysis to unlock valuable insights (GeeksforGeeks, 2023). Stakeholders must recognize these scale-related challenges and leverage appropriate tools to harness the full potential of big data for informed decision-making.

8 References GeeksforGeeks. (2023, May 13). Difference between Traditional data and Big data . https://www.geeksforgeeks.org/difference-between-traditional-data-and-big-data/ Harms, C. (2023, October 3). Cloud computing in healthcare: benefits & risks . AIM Consulting. https://aimconsulting.com/insights/healthcare-cloud-computing-benefits-risks/ LaunchDarkly. (2022, October 4). 5 Cloud Deployment Models: Which One is Right for You? LaunchDarkly . Retrieved January 4, 2024, from https://launchdarkly.com/blog/cloud- deployment-models-explaining-and-comparing-the/

DAT 260 Project One_

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