Assignment Instructions on Big Data Analytics Using Hadoop and Spark
Assignment 9
General Assessment Guidance
This assignment constitutes the principal evaluation for the module and explores practical and theoretical aspects of big data analytics. Students are expected to engage critically with Hadoop and Spark frameworks, analyzing how these technologies enable large-scale data processing, real-time analytics, and actionable insights for organizations.
All submissions must be uploaded via Turnitin online access. Submissions through email, hard copy, or portable storage devices will not be accepted. Late submissions will receive a mark of zero.
Do not include personal identifiers, only your Student Reference Number (SRN). Harvard referencing is mandatory; failure to properly cite sources will be treated as plagiarism. AI tools may only be used for language correction or draft review, not for creating analytical content.
A completed Assignment Cover Sheet must accompany your submission to ensure administrative validity.
Assessment Brief
Exploring Large-Scale Data Analytics
This assignment requires a comprehensive consultancy-style report examining the use of Hadoop and Spark in data-intensive environments. Students will act as consultants for a hypothetical organization seeking insights into big data analytics for operational efficiency, strategic decision-making, or market analysis.
The report should include analysis of distributed computing principles, data ingestion, storage, and real-time processing, while also discussing technical limitations, scalability, and the trade-offs between batch and streaming analytics.
Evidence-based recommendations must integrate academic research, case studies, and industry examples, highlighting practical relevance to contemporary U.S. businesses. Students should also consider ethical, regulatory, and security aspects of big data analytics.
Learning Outcomes
LO1 – Understand and explain the architecture and functionality of Hadoop and Spark ecosystems.
LO2 – Critically assess the challenges and opportunities of implementing big data analytics in organizational settings.
LO3 – Apply analytical frameworks to evaluate data processing strategies, including distributed computing and real-time analytics.
LO4 – Develop actionable, evidence-based recommendations for organizational adoption of big data technologies.
Key Sections of the Report
- Executive Synopsis of Big Data Initiatives
- Data Architecture and Framework Overview
- Challenges in Distributed Data Processing
- Analytical Approaches and Comparative Evaluation
- Data Governance
- Integrating Case Studies and Secondary Data Insights
- Strategic Recommendations for Big Data Deployment
Each section should demonstrate critical reasoning, use empirical evidence, and avoid unsupported opinions.
Suggested Report Structure
- Declaration Page (PP)
• Title Page
• Table of Contents
• Executive Synopsis of Big Data Initiatives
• Data Architecture and Framework Overview
• Challenges in Distributed Data Processing
• Analytical Approaches and Comparative Evaluation
• Stakeholder Implications and Data Governance
• Integrating Case Studies and Secondary Data Insights
• Strategic Recommendations for Big Data Deployment
• Harvard References
• Appendices (if required)
Word Count Breakdown (Approximate)
Executive Synopsis – 300
Data Architecture and Framework Overview – 400
Challenges in Distributed Data Processing – 400
Analytical Approaches and Comparative Evaluation – 500
Stakeholder Implications and Data Governance – 300
Integrating Case Studies and Secondary Data Insights – 400
Strategic Recommendations for Big Data Deployment – 300
Total – approximately 2,600 words
Word allocations are indicative. Analytical depth and evidence-based reasoning are prioritized over strict word limits.
Executive Synopsis of Big Data Initiatives
Provide a high-level overview of the report, summarizing the organization’s objectives in leveraging big data, the technologies under review (Hadoop and Spark), and the anticipated outcomes. Highlight the significance of real-time vs. batch processing, distributed storage, and predictive analytics capabilities.
Data Architecture and Framework Overview
Examine the technical components of Hadoop (HDFS, MapReduce, YARN) and Spark (RDDs, DataFrames, Spark SQL, Spark Streaming). Discuss data ingestion, storage, and processing workflows, including considerations for scalability, fault tolerance, and cluster management. Highlight differences and complementarities between Hadoop and Spark.
Include diagrams or flowcharts to illustrate architecture if appropriate. Reference recent literature to demonstrate familiarity with current trends in big data frameworks.
Challenges in Distributed Data Processing
Critically analyze technical, organizational, and operational challenges. Consider issues such as:
- Data volume, velocity, and variety
- Fault tolerance and resource allocation
- Cluster configuration complexities
- Data consistency, latency, and throughput
Provide examples from real-world industries to illustrate practical obstacles and mitigation strategies.
Analytical Approaches and Comparative Evaluation
Apply analytical frameworks to compare Hadoop and Spark capabilities. Discuss batch vs. real-time processing, machine learning integration, and streaming analytics. Evaluate performance metrics, including execution time, memory usage, and cost efficiency. Integrate insights from academic studies or benchmark reports.
Data Governance
Identify stakeholders impacted by big data initiatives, including data engineers, analysts, managers, IT security personnel, and end users. Examine how governance policies, regulatory compliance (e.g., GDPR, HIPAA), and ethical considerations influence system design, data access, and analytics outcomes.
Integrating Case Studies and Secondary Data Insights
Critically synthesize empirical evidence from industry case studies and academic research. Highlight successes and failures of big data projects in sectors such as finance, healthcare, and e-commerce. Discuss limitations of secondary data and potential biases in reported outcomes.
Strategic Recommendations for Big Data Deployment
Provide actionable, evidence-based recommendations for organizations adopting Hadoop and Spark. Consider implementation planning, resource allocation, talent requirements, cost-benefit analysis, and integration with existing IT infrastructure. Highlight how organizations can maximize ROI, operational efficiency, and competitive advantage through effective big data analytics.
References and Presentation
- Use Harvard referencing consistently. Include academic journals, reputable industry reports, and authoritative books.
- Maintain professional formatting, numbered pages, and correctly labelled figures/tables.
- Prioritize critical analysis, theoretical insight, and empirical evidence.