13 Mar DAT 260 Project One: Exploring Cloud Computing & Big Data Challenges
Project One Overview & ExpectationsAssignment Title Variations (from student docs): Cloud Computing Evaluation Paper
Feasibility Proposal on Cloud Computing Models
Exploring Cloud Computing & Big Data Challenges
Key Requirements (based on templates and examples): 6–10 pages (including cover, sections, references).
Structured sections: Introduction, Cloud Computing Overview (deployment & service models), Benefits & Drawbacks, Risks/Challenges, Big Data Implications (structured vs. big/unstructured data), Recommendations/Feasibility, Conclusion.
Choose or assume an organization/industry (e.g., healthcare, retail, finance, nonprofit) to contextualize recommendations.
Discuss NIST-aligned models + big data challenges (volume, velocity, variety, veracity).
Include comparisons (tables), real-world examples, and 2025–2026 trends/stats.
Cite sources: Textbook (Big Data, Big Analytics), NIST SP 800-145, industry reports (Gartner, Flexera), articles.
Often uses a provided Project One Template with headings like: Cloud Computing
Benefits and Drawbacks
Considerations/Risks
Big Data Challenges
Recommendations
Learning Objectives Synthesize Modules 1–3: Cloud models (Module 1), migration SWOT (Module 2), big data tools (Module 3).
Evaluate cloud feasibility for big data environments.
Analyze trade-offs: cost vs. security, scalability vs. control, structured vs. unstructured data storage.
Propose suitable models for an organization facing big data growth.
Study Strategy Use the official template (download from course).
Pick a specific organization early (e.g., mid-sized e-commerce retailer handling customer logs, transactions, and IoT data).
Reuse content from prior modules (e.g., deployment models from Module 1, SWOT insights from Module 2).
Include at least one table for models comparison and one for structured vs. big data.
Aim for balanced coverage: 40% cloud models, 30% benefits/risks, 20% big data challenges, 10% recommendations.
Core Content Outline1. Cloud Computing OverviewDeployment Models (NIST-based; often list 4–5): Public: Multi-tenant, provider-managed (AWS, Azure, GCP). Low cost, high scalability.
Private: Single-tenant, on-prem or hosted. High control/security.
Hybrid: Combines public + private (e.g., sensitive data private, burst compute public).
Community: Shared among similar orgs (e.g., government, healthcare consortia).
Multi-cloud (emerging): Multiple public providers to avoid lock-in (common in 2026).
Service Models: IaaS (infrastructure), PaaS (platform), SaaS (software).
2026 Stats: ~95% enterprises use cloud; hybrid/multi-cloud dominant (80–90%); public cloud spend ~$600B+.
2. Benefits & Drawbacks of Cloud ModelsBenefits Scalability/elasticity for big data growth (handle volume/velocity).
Cost shift: OpEx vs. CapEx; pay-as-you-go.
Accessibility/global reach; disaster recovery built-in.
Faster deployment of analytics tools (Spark, Hive on cloud).
Innovation enablement (AI/ML services).
Drawbacks Vendor lock-in (proprietary APIs).
Potential latency in hybrid setups.
Internet dependency.
Higher initial migration costs (“migration tax”).
3. Risks & ConsiderationsSecurity/Privacy: Data breaches, shared infrastructure risks (public); misconfigurations common.
Compliance: GDPR, HIPAA, data sovereignty (harder in public/multi-region).
Cost Overruns: Unpredictable billing (84% orgs struggle per 2026 reports).
Downtime/Outages: Provider failures affect availability.
Skills Gap: Need cloud/DevOps expertise.
4. Big Data Challenges & Cloud ImpactStructured vs. Big/Unstructured Data Structured Data: Fixed schema (tables, relational DBs like SQL Server, PostgreSQL). Easier querying (SQL).
Fits traditional RDBMS or cloud SQL services.
Challenges: Limited for variety/velocity.
Big Data (Unstructured/Semi-structured): Logs, JSON, images, sensor data (3–5 Vs). Requires flexible storage (data lakes: S3, ADLS).
Challenges: Volume overwhelms on-prem; veracity/quality issues; processing complexity.
How Cloud Addresses Challenges Elastic compute/storage scales for volume.
Data lakes + lakehouses (Delta Lake, Iceberg) unify structured/unstructured.
Tools integration: Spark on EMR/Databricks handles variety/velocity.
Remaining Challenges: Data gravity (moving large datasets costly), governance in lakes, quality management.
Quick Comparison Table (Include in Paper)Aspect
Structured Data (Traditional)
Big/Unstructured Data (Big Data)
Cloud Advantage
Storage
Relational DBs
Data lakes/files
Scalable object storage (S3, Blob)
Processing
SQL queries
Batch/streaming (Spark, Flink)
Managed services reduce overhead
Scalability
Vertical
Horizontal
Elastic (auto-scaling clusters)
Challenges
Schema rigidity
Variety/veracity
Schema-on-read + governance tools
Best Cloud Fit
RDS, Cloud SQL
Data Lake + Spark
Hybrid for sensitive + elastic analytics
5. Recommendations & FeasibilityRecommend hybrid/multi-cloud for most orgs: Keep sensitive/structured data private/on-prem, leverage public for big data analytics.
Phased migration: Start with non-critical workloads (e.g., dev/test).
Use managed services (Databricks, Snowflake) to ease big data handling.
Feasibility: High for growing data volumes; ROI via cost savings + faster insights (cite 20–50% efficiency gains).
Tie to org example: E.g., retail → hybrid for transaction security + public lake for customer behavior analytics.
Key 2025–2026 Trends & Stats (Cite!)Cloud market: ~$900B–$1T spend.
90%+ enterprises hybrid/multi-cloud.
Big data in cloud: 95% new workloads cloud-native.
Challenges persist: Cost management (top issue), security incidents rising.
Future: AI-driven cloud ops, edge integration for velocity.
Tips for High-Quality SubmissionUse headings from template for structure.
Include 2–3 tables/figures (models, pros/cons, structured vs. big data).
Add real examples: Netflix (public/AWS for big data streaming), banks (hybrid for compliance).
Reflection: Discuss how cloud enables emerging tech (AI/ML on big data).
References: 8–12 sources (NIST, textbook, Gartner/Flexera 2025–2026 reports).
Length/Balance: Intro + conclusion ~1 page each; main sections detailed.
Quick Study Checklist
□ Download/use official template.
□ Select org/industry + scenario.
□ Cover all models + service types.
□ Compare structured/big data explicitly.
□ Add tables, examples, stats.
□ Proofread; ensure feasibility argument is clear.These notes provide a ready framework to complete Project One. Integrate prior module knowledge for cohesion. Good luck with DAT 260 Project One!
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