In The First Half Of The Course, We Focused On Big Data Anal ✓ Solved

In the first half of the course we focused on Big Data Analy

In the first half of the course we focused on Big Data Analytics and in the second half we focused on Blockchain. This discussion is reflective and should use your own words (you may include citations). Address the following prompts: (a) What were some of the more interesting assignments to you? (b) What reading(s) did you find most interesting and why? (c) How has this course changed your perspective? (d) What topics or activities would you add to the course, or should be focused on more?

Paper For Above Instructions

Introduction

This course brought together two transformative technologies: Big Data Analytics and Blockchain. Reflecting on my learning, I consider how assignments and readings shaped my understanding, how my perspective evolved, and what course refinements would make future cohorts more prepared for real-world applications. Below I summarize the most engaging assignments, the readings that resonated most, the shift in my perspective, and recommended course enhancements.

Interesting Assignments

Two types of assignments stood out as particularly valuable. First, hands-on data processing projects that required ingesting, cleaning, and analyzing large datasets were highly instructive. Building a scalable ETL pipeline and applying distributed processing exposed practical challenges—data skew, I/O bottlenecks, schema evolution, and the importance of reproducible pipelines (Manyika et al., 2011; Katal et al., 2013). These tasks reinforced theoretical knowledge about Hadoop, Spark, and pipeline orchestration with tangible troubleshooting experience.

Second, the blockchain lab—creating a Solidity smart contract and deploying it to a testnet—was transformative. Writing a contract, testing edge cases, and seeing how immutability affects upgrade paths highlighted development trade-offs (Buterin, 2014; Crosby et al., 2016). This assignment made the abstract principles of decentralization and trustless execution concrete and exposed the need for formal verification and robust testing in smart contract development (Zyskind et al., 2015).

Most Interesting Readings and Why

Certain readings deepened my appreciation of both domains. For big data, the McKinsey report (Manyika et al., 2011) and McAfee & Brynjolfsson’s overview (2012) were compelling because they linked technical capabilities to economic and organizational impact, showing why investment in data infrastructure and talent matters. Katal et al. (2013) provided a clear taxonomy of challenges—volume, velocity, variety, veracity—and practical tools to address them, making technical complexity approachable.

For blockchain, Nakamoto’s Bitcoin whitepaper (2008) and Buterin’s Ethereum whitepaper (2014) were essential readings. Nakamoto’s work illuminated how cryptographic primitives and distributed consensus can replace centralized trust, while Buterin’s paper demonstrated how programmability extends those ideas to general-purpose decentralized applications. Crosby et al. (2016) offered a pragmatic taxonomy of blockchain use cases beyond cryptocurrency, useful for evaluating applicability in industries such as supply chain, healthcare, and identity management.

How the Course Changed My Perspective

Before the course, I viewed big data primarily as a storage and analytics problem and blockchain largely as a cryptocurrency curiosity. The curriculum corrected both misconceptions. I now see big data as an ecosystem requiring governance, ethical considerations, and interdisciplinary teams—technical solutions must be paired with policy and domain knowledge to extract value responsibly (Wang, Kung, & Byrd, 2018). Likewise, blockchain is not a universal solution but a design pattern for specific problems where decentralization, verifiability, and tamper-resistance create clear value (Crosby et al., 2016).

Importantly, the intersection of the two fields is where I see compelling innovation: using blockchain for auditable data provenance, consented data sharing, and decentralized identity layered atop big data analytics pipelines (Zyskind et al., 2015). This combined view underscores that technology selection must consider governance, privacy, and incentives as much as scalability and performance.

Course Feedback and Recommendations

To strengthen future offerings, I recommend these additions and emphases:

  • Practical privacy and governance modules: Add sessions on data protection regulations (e.g., GDPR), differential privacy, and privacy-preserving analytics. Students should practice anonymization techniques and threat models to understand trade-offs between utility and privacy (Katal et al., 2013; Zyskind et al., 2015).

  • Security and formal methods for smart contracts: Given the irreversible nature of deployed contracts, integrate tools for formal verification, static analysis, and secure design patterns so students understand common vulnerabilities and mitigations (Buterin, 2014).

  • Cross-disciplinary capstone: A term-long team project that requires combining big data pipelines with a blockchain-based provenance or consent layer would solidify learning across domains and emphasize systems thinking.

  • Industry guest lectures and case studies: Bring practitioners who have implemented enterprise big data platforms or blockchain pilots to discuss real-world constraints like legacy systems, stakeholder incentives, and operational costs (PwC, 2018).

  • Career and ethical discussions: Provide guidance on career pathways and professional ethics in data science and decentralized systems. This prepares students for roles that balance innovation with responsibility (McAfee & Brynjolfsson, 2012).

Conclusion

The course successfully bridged conceptual frameworks and hands-on practice. Assignments that demanded real-world troubleshooting and smart-contract development were most impactful, while readings linking technology to economic and governance realities shaped a mature perspective. Moving forward, incorporating privacy, security, interdisciplinary capstones, and practitioner insights will produce graduates who can design, deploy, and govern data-driven and decentralized systems responsibly.

References

  1. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
  2. McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.
  3. Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: Issues, challenges, tools and good practices. 2013 International Conference on Emerging Trends and Applications in Computer Science (ICEACS).
  4. Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3–13.
  5. Zikopoulos, P., Eaton, C., deRoos, D., Deutsch, T., & Lapis, G. (2012). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill.
  6. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. (White paper).
  7. Buterin, V. (2014). A next-generation smart contract and decentralized application platform. Ethereum White Paper.
  8. Crosby, M., Pattanayak, P., Verma, S., & Kalyanaraman, V. (2016). Blockchain technology: Beyond bitcoin. Applied Innovation Review, 2, 6–10.
  9. Zyskind, G., Nathan, O., & Pentland, A. (2015). Decentralizing privacy: Using blockchain to protect personal data. 2015 IEEE Security and Privacy Workshops.
  10. PricewaterhouseCoopers (PwC). (2018). PwC Global Blockchain Survey: Blockchain is here. What’s your next move? PwC.

Related Assignments