Class Guide: Overview & Learning Objectives for Module 1
DAT 260 Module 1 Journal: Exploring Cloud Deployment Models (Southern New Hampshire University – Emerging Technologies and Big Data course).
Class Guide: Overview & Learning Objectives for Module 1Course Context
DAT 260 introduces emerging technologies that support big data and analytics, with Module 1 focusing on cloud computing foundations — especially deployment models. This sets the stage for later modules on big data tools, AI/ML integration, and SWOT analyses of cloud adoption.Key Learning Objectives Define and distinguish the four NIST-standard cloud deployment models (public, private, community, hybrid).
Explain advantages, disadvantages, costs, security, scalability, and control for each model.
Identify real-world use cases, especially those relevant to data analytics, big data processing, and organizational decision-making.
Reflect on how deployment choice affects data workflows, compliance (e.g., GDPR, HIPAA), cost management, and user experience.
Use current (2025–2026) market statistics to support arguments about adoption trends.
Journal Assignment Expectations (typical from student submissions & rubrics) 800–1500 words (check exact rubric).
Structured discussion of models (definitions + pros/cons + examples).
Personal or professional reflection on impact (e.g., “from a user’s perspective”).
Cite NIST SP 800-145 and recent sources.
Often includes: how adoption affects users, productivity, or data handling in analytics contexts.
Study Strategy Read NIST SP 800-145 (core definitions).
Compare models using a table (included below).
Memorize 2–3 examples per model.
Note 2025–2026 stats for credibility.
Practice reflection: link to big data/analytics (scalability for ML training, cost for large datasets, security for sensitive data).
Study Notes: Core Content Outline1. NIST Definition of Cloud Computing (Foundation)Five Essential Characteristics: On-demand self-service, broad network access, resource pooling, rapid elasticity, measured service.
Deployment Models determine who owns/controls the infrastructure and who can access it (NIST SP 800-145, 2011 – still the standard in 2026).
2. The Four Main Cloud Deployment ModelsPublic Cloud Definition — Infrastructure provisioned for open use by the general public. Owned/operated by cloud provider (exists on provider premises). Multi-tenant.
Providers → AWS, Microsoft Azure, Google Cloud Platform (GCP).
Advantages Low/no upfront cost (pay-as-you-go).
Massive scalability & elasticity.
Provider handles maintenance & updates.
Global reach & high availability.
Disadvantages Less control & customization.
Shared infrastructure → potential “noisy neighbor” performance issues.
Security/compliance concerns for sensitive data.
Best for → Startups, variable workloads, development/testing, big data analytics with burst needs (e.g., training ML models).
2026 Stats → ~96% of companies use public cloud; AWS holds ~32% market share.
Examples → Netflix (streaming scalability), Airbnb (demand spikes), many analytics teams using BigQuery or Snowflake.
Private Cloud Definition — Exclusive use by single organization (multiple consumers like business units). Can be on-premises or hosted by third party. Single-tenant.
Advantages Highest control & customization.
Superior security/privacy (ideal for regulated data).
Predictable performance (no shared resources).
Easier compliance (HIPAA, PCI-DSS, data sovereignty).
Disadvantages High upfront & ongoing costs (hardware, staff).
Limited scalability (tied to owned capacity).
Slower to deploy changes.
Best for → Banks, healthcare, government, organizations with sensitive/legacy data.
2026 Stats → Private cloud market ~$114–150B in 2024–2025, growing to ~$195B by 2030 (CAGR ~9%). ~84% of companies use private cloud in some form.
Examples → Bank of America (customer data), major hospitals (PHI).
Community Cloud Definition — Exclusive use by specific community with shared concerns (mission, security, compliance). Owned/managed by community members or third party.
Advantages Cost sharing among similar organizations.
Tailored security & compliance.
Better collaboration within group.
Disadvantages Limited scalability.
Governance can be complex (shared decisions).
Less flexible than public/hybrid.
Best for → Government agencies, healthcare consortia, research institutions, industry groups.
Examples → U.S. federal agencies sharing compliant infrastructure; university research alliances.
Hybrid Cloud Definition — Composition of two or more distinct clouds (public + private/community) bound by technology enabling portability (e.g., cloud bursting).
Advantages Best of both worlds: public scalability + private security.
Cost optimization (burst to public during peaks).
Flexibility & phased migration.
Avoid vendor lock-in.
Disadvantages Complex management & integration.
Potential latency/security risks in data movement.
Higher operational overhead.
Best for → Large enterprises, data analytics teams needing both secure storage and elastic compute (e.g., keep raw data private, analyze in public).
2026 Stats → Dominant strategy: 70–90% of enterprises use hybrid/multi-cloud; hybrid market growing rapidly (~24% CAGR projected).
Examples → Delta Air Lines (operations + public analytics); Azure Stack/VMware on-prem + public burst.
3. Quick Comparison Table (Memorize or Include in Journal)Criterion
Public Cloud
Private Cloud
Community Cloud
Hybrid Cloud
Cost
Low (pay-go)
High
Shared (moderate)
Optimized
Scalability
Very high
Limited
Moderate
Very high (burst)
Security/Control
Lower
Highest
High (shared)
Balanced
Compliance ease
Challenging
Easiest
Good (tailored)
Complex
Best Use Case
Variable workloads, startups, analytics burst
Regulated/sensitive data
Industry groups
Enterprises, mixed needs
2026 Adoption Trend
Widespread base
Niche high-security
Limited
Fastest growing
4. Key 2025–2026 Trends & Statistics (Use in Journal)Cloud market → ~$947 billion by 2026.
96% companies use public cloud; 84% use private in some capacity.
Hybrid/multi-cloud → 80–90% of enterprises; reduces lock-in & improves resilience.
Multi-cloud use rising (multiple providers) for best-of-breed services.
Security remains top concern (60–80% report incidents).
Analytics relevance → Public/hybrid ideal for big data processing (elastic compute for Spark, ML training).
5. Reflection Tips for JournalDiscuss user perspective: ease of access (public), control (private), collaboration (community), flexibility (hybrid).
Link to productivity/big data: e.g., public cloud enables faster insights without hardware delays.
Personal example: If you’ve used Google Drive (public), Dropbox Business (private-ish), or AWS + on-prem (hybrid).
Future outlook: Hybrid/multi-cloud as standard for data-driven organizations.
Quick Study Checklist
□ Memorize NIST definitions verbatim (key for grading).
□ Know 1–2 examples per model.
□ Understand trade-offs (cost vs. control vs. scale).
□ Include 2–3 recent stats.
□ Practice writing 300–400 word section per model + reflection.Use these notes to structure your journal: Introduction → Model sections → Comparison → Reflection → Conclusion. Good luck with DAT 260 Module 1!
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