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<b> ASSIGNMENT 2 FRONT SHEET </b>

<b>Qualification BTEC Level 5 HND Diploma in Computing </b>

<b>Unit number and title </b> Unit 06: Planning a computing project

<b>Submission date </b> 7-4-2024 <b>Date Received 1st submission </b>

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<b> Summative Feedback:  Resubmission Feedback:</b>

<b>IV Signature:</b>

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<b>Table of Contents </b>

A: Introduction ... 5

I: Project purpose: ... 6

II: The objectives of the project ... 7

P5: Devise comprehensive project plans for a chosen scenario, including a work and resource allocation breakdown using appropriate tools. ... 8

1. Overview: ... 8

2. Project Scope and Deliverables: ... 8

3. Work Breakdown Structure (WBS): ... 9

4. Work timeline: ... 12

P6 Communicate appropriate project recommendations for technical and nontechnical audiences. ... 16

1: Stakeholders ... 16

2: Project Recommendations for Technical Audience: ... 17

3: Project Recommendations for Non-Technical Audience ... 18

P7 Present arguments for the planning decisions made when developing the project plans. ... 20

P8 Discuss accuracy and reliability of the different research methods applied. ... 25

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<b>Table of Figures </b>

Figure 1: My WBS ... 11

Figure 2: Gantt Chart 1 ... 12

Figure 3: Gantt Chart 2 ... 12

Figure 4: Gantt Chart 3 ... 13

Figure 5: Gantt Chart 4 ... 13

Figure 6: Gantt Chart 5 ... 14

Figure 7: Gantt Chart 6 ... 14

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<b>A: Introduction </b>

In recent years, the surge in digital technologies has catalyzed an unprecedented influx of data across various sectors, including academia. This deluge of information, commonly referred to as Big Data, presents a wealth of opportunities for educational institutions seeking to enhance their

operational efficiency.

The proliferation of Big Data technologies offers academia a powerful toolset characterized by its capacity to collect, process, and analyze vast and intricate datasets. These technologies enable educational institutions to extract valuable insights, make data-driven decisions, and streamline processes like never before. Leveraging advanced data analytics techniques such as predictive modeling, machine learning, and natural language processing empowers institutions to discern meaningful patterns and trends within their data, thereby optimizing resource allocation, enhancing student outcomes, and bolstering administrative efficacy.

Moreover, the application of Big Data analytics holds immense potential in the realm of academic performance analysis. By scrutinizing student performance data, institutions can discern trends indicative of success or struggle, thus enabling the implementation of personalized support mechanisms and interventions. Such a data-centric approach not only enriches the learning experience but also cultivates improved student outcomes, fostering a culture of continuous academic enhancement.

However, despite the promise and potential of Big Data technologies, their integration into academic settings is not devoid of challenges. Privacy and security concerns surrounding student data demand meticulous attention to ensure compliance with pertinent regulations and safeguard sensitive information. Additionally, issues pertaining to data quality and integration necessitate concerted efforts to harmonize diverse data sources for effective analysis. Furthermore, the successful implementation of Big Data technologies hinges upon the availability of skilled data analysts and robust IT infrastructure.

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<b>B: ContentsI: Project purpose: </b>

The potential of applying cloud computing for processing large datasets stored on a cloud system is profound and transformative. Cloud computing represents a paradigm shift in how organizations approach data processing, offering unparalleled scalability and flexibility. By harnessing the power of cloud resources, institutions can effectively manage vast amounts of data without the need for costly infrastructure investments. This scalability enables organizations to adapt to fluctuating data volumes seamlessly, ensuring that processing capabilities align with evolving needs.

One of the most compelling aspects of cloud computing is its cost-effectiveness. Traditional data processing methods often require significant upfront investments in hardware and infrastructure. In contrast, cloud computing operates on a pay-as-you-go model, allowing organizations to scale resources up or down based on demand. This not only reduces initial capital expenditures but also optimizes operational costs over time, making data processing more accessible and affordable. Furthermore, cloud computing offers advanced tools and technologies for analyzing data. From sophisticated analytics platforms to machine learning algorithms, cloud service providers equip researchers with the means to extract valuable insights from large datasets efficiently. These analytical capabilities empower organizations to make data-driven decisions, driving innovation and competitive advantage.

Security is paramount in the realm of data processing, especially when dealing with sensitive

information. Cloud service providers prioritize security measures, implementing robust encryption, access controls, and compliance frameworks to safeguard data integrity and confidentiality. By entrusting data to reputable cloud platforms, organizations can mitigate security risks and ensure compliance with regulatory requirements.

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<b>II: The objectives of the project </b>

The objectives of the project focused on harnessing the potential of cloud computing for processing large datasets stored on a cloud system are as follows:

<b>Evaluate Cloud Computing Platforms: Conduct thorough research and evaluation of various cloud </b>

computing platforms (e.g., AWS, Azure, GCP) to identify the most suitable platform for processing large datasets efficiently.

<b>Design Scalable Infrastructure: Design and deploy a scalable cloud infrastructure capable of </b>

storing and processing large datasets. This includes setting up virtual machines, storage solutions, networking components, and security measures.

<b>Develop Optimized Algorithms: Develop and optimize data processing algorithms or workflows </b>

specifically tailored for cloud environments. Utilize parallel processing techniques and distributed computing frameworks to maximize performance and efficiency.

<b>Implement Data Processing Workflows: Implement developed algorithms and workflows on the </b>

selected cloud platform. Ensure seamless integration with cloud services and optimize configurations for efficient data processing.

<b>Document Best Practices: Document best practices, guidelines, and recommendations for </b>

leveraging cloud computing for processing large datasets. This includes infrastructure setup procedures, algorithm design principles, cost management strategies, and security considerations.

<b>Facilitate Knowledge Transfer: Provide user manuals, guides, and training sessions to facilitate </b>

knowledge transfer and enable stakeholders to leverage cloud computing effectively for large dataset processing tasks.

<b>Deliver Comprehensive Reports: Compile research findings, infrastructure setup details, </b>

algorithm implementations, testing results, and best practices into comprehensive reports. Present key insights, recommendations, and lessons learned to stakeholders for informed decision-making. By accomplishing these objectives, the project aims to empower organizations with the knowledge, tools, and capabilities needed to harness the full potential of cloud computing for processing large datasets efficiently and effectively.

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<b>P5: Devise comprehensive project plans for a chosen scenario, including a work and resource allocation breakdown using appropriate tools. </b>

<b>1. Overview: </b>

The exponential growth of data in various domains, including but not limited to business, science, and technology, has led to an increased demand for efficient methods of processing and analyzing large datasets. Traditional computing infrastructures often struggle to keep up with the scale and complexity of these datasets, resulting in performance bottlenecks and increased processing times. Cloud computing, with its scalable and on-demand resources, offers a promising solution to this challenge. By leveraging cloud-based infrastructure and services, organizations can efficiently store, process, and analyze large datasets without the need for significant upfront investments in

hardware and infrastructure.

This project seeks to explore the potential of applying cloud computing technologies for processing large datasets stored on cloud systems. By harnessing the scalability, flexibility, and cost-

effectiveness of cloud computing platforms such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP), the project aims to develop optimized solutions for handling massive datasets efficiently. Through a combination of research, infrastructure setup, algorithm development, testing, and documentation, the project will provide insights and guidelines for organizations looking to leverage cloud computing for large-scale data processing tasks.

<b>2. Project Scope and Deliverables: Project Scope: </b>

Research and Evaluation: Conduct comprehensive research on various cloud computing platforms and their suitability for large dataset processing. Evaluate factors such as scalability, performance, cost, and ease of use.

Infrastructure Setup: Design and deploy a scalable cloud infrastructure capable of storing and processing large datasets. Configure security measures, data backup mechanisms, and monitoring tools to ensure data integrity and availability.

Algorithm Development: Develop and optimize data processing algorithms or workflows specifically designed for cloud environments. Utilize parallel processing techniques, distributed computing frameworks (e.g., Apache Spark), and cloud-native services to maximize performance and efficiency. Deployment and Testing: Deploy developed algorithms on the cloud infrastructure and conduct rigorous performance testing. Evaluate factors such as processing speed, scalability, resource utilization, and cost-effectiveness. Identify and address any bottlenecks or performance issues.

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<b>Deliverables: </b>

Research Report: A comprehensive report detailing the research findings on various cloud

computing platforms and their suitability for large dataset processing. This report will include an analysis of key factors such as scalability, performance, cost, and ease of use.

Infrastructure Setup Documentation: Detailed documentation outlining the setup procedures and configurations for the cloud infrastructure. This documentation will cover aspects such as virtual machine provisioning, storage configuration, network setup, security measures, and monitoring tools.

Algorithm Implementation: Developed data processing algorithms or workflows optimized for cloud environments. This includes code repositories, implementation details, and integration with cloud services.

Best Practices Documentation: Guidelines, best practices, and recommendations for leveraging cloud computing for large dataset processing. This documentation will cover topics such as algorithm design, infrastructure optimization, cost management, and security considerations. By delivering these comprehensive project scope and associated deliverables, the project aims to provide valuable insights and actionable recommendations for organizations seeking to harness the potential of cloud computing for processing large datasets effectively.

<b>3. Work Breakdown Structure (WBS): </b>

WBS stands for Work Breakdown Structure. It is a project management technique used to break down a project or work scope into smaller, more manageable components. The purpose of creating a WBS is to organize and define the work required to complete the project.

The WBS is typically represented as a hierarchical structure, starting with the highest level, which represents the main deliverables or phases of the project. Each subsequent level represents a further breakdown of the deliverables into smaller and more specific components. The lowest level of the WBS consists of work packages or tasks that can be assigned to individuals or teams for execution.

The WBS provides a visual representation of the project's scope and helps in understanding the relationship between different components of the project. It enables project managers to effectively plan, schedule, and control the project by identifying all necessary work and ensuring that it is assigned to the appropriate resources.

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To create a WBS for your project, you’ll need information from other project management documents.

Here are six simple steps to create a work breakdown structure.

<b>1: Define Project Objectives and Scope </b>

• Understand the overarching goal of the project, which in this case is to explore and leverage cloud computing for processing large datasets stored on a cloud system.

• Define the scope of the project, including the specific tasks and deliverables.

<b>2: Identify Major Deliverables </b>

• Determine the key deliverables of the project. This may include research reports,

infrastructure setup, algorithm development, testing results, documentation, and training materials.

<b>3: Break Down Deliverables into Sub-Deliverables </b>

• Decompose each major deliverable into smaller, manageable components. For example, infrastructure setup may include tasks such as designing architecture, setting up virtual machines, and configuring security measures.

<b>4: Identify Work Packages </b>

• Break down sub-deliverables into work packages, which are the lowest level of tasks in the WBS. These are actionable items that can be assigned to team members and tracked

individually.

<b>5: Assign Responsibility and Resources </b>

• Determine who will be responsible for each work package and allocate the necessary resources, including personnel, time, and budget.

<b>6: Review and Validate </b>

• Review the WBS with key stakeholders to ensure that all tasks are captured and properly organized.

• Validate the WBS against project objectives, scope, and constraints to ensure completeness and accuracy.

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<i>Figure 1: My WBS </i>

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<b>4. Work timeline: </b>

I will use Gantt chart software to create a Work timeline for my project.

<b>Milestone 1: Research Initiation (Month 1) </b>

Objective: Establish a clear direction for the research study and define its scope. Stakeholders: Research team, project manager, sponsors.

Result: The research objectives and scope were clearly defined, existing literature was reviewed, and key research questions and hypotheses were identified, providing a solid foundation for the subsequent phases.

<i>Figure 2: Gantt Chart 1 </i>

<b>Milestone 2: Data Collection Phase (Month 2) </b>

Objective: Gather relevant data and resources necessary for the research. Stakeholders: Research team, data providers, project manager.

Result: Data on cloud computing platforms and datasets for research were collected, along with identification of relevant case studies, enabling the research to proceed with adequate resources and examples.

<i>Figure 3: Gantt Chart 2 </i>

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<b>Milestone 3: Literature Review and Analysis (Month 3) </b>

Objective: Analyze existing research to identify trends, challenges, and opportunities in cloud-based data processing.

Stakeholders: Research team, academic community, project manager.

Result: Existing literature and case studies were thoroughly analyzed, providing insights into current trends, challenges, and opportunities in cloud-based data processing, which guided the direction of the research.

<i>Figure 4: Gantt Chart 3 </i>

<b>Milestone 4: Research Methodology Development (Month 4) </b>

Objective: Design the methodology for the research study and establish criteria for evaluating based solutions.

cloud-Stakeholders: Research team, project manager, evaluators.

Result: A robust research methodology was developed, along with criteria for evaluating based solutions, ensuring the research study's credibility and effectiveness.

<i>cloud-Figure 5: Gantt Chart 4 </i>

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<b>Milestone 5: Data Analysis and Interpretation (Month 6) </b>

Objective: Analyze collected data and interpret findings in relation to research objectives. Stakeholders: Research team, data analysts, project manager.

Result: Data from literature review and case studies were analyzed, findings were interpreted, and gaps in existing knowledge were identified, laying the groundwork for further investigation and analysis.

<i>Figure 6: Gantt Chart 5 </i>

<b>Milestone 6: Report Writing and Documentation (Month 7) </b>

Objective: Summarize research findings, document methodologies, and provide recommendations. Stakeholders: Research team, project manager, sponsors, stakeholders.

Result: Research findings were summarized in a comprehensive report, methodologies and analyses were documented, and insights and recommendations were provided, fulfilling the objectives of the research study.

<i>Figure 7: Gantt Chart 6 </i>

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