13.6.3: Hands-On Lab Questions

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Page ID: 58355

Felix Amoruwa
De Anza College

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Lab: Designing a Cloud Analytics Solution

Scenario:

GreenEnergy Corp is a renewable energy company with: 500 wind turbines generating IoT sensor data (temperature, vibration, power output — 1 reading/second/turbine), 3 years of historical operational data (2TB), a finance team needing monthly reporting, and data scientists building predictive maintenance models.

Instructions:

Architecture Design (25 min): Design a cloud analytics architecture on AWS, Azure, or GCP. Include: IoT data ingestion, data lake storage, ETL/processing, data warehouse for reporting, ML model training, and BI dashboards. Create an architecture diagram.
Service Selection (15 min): For each component, select a specific cloud service. Create a table: Component | Cloud Service | Why This Service | Alternative Considered.
Cost Optimization (20 min): Estimate monthly costs and propose optimization strategies: which data goes to which storage tier? Where would spot instances be appropriate? What reserved capacity is needed for steady workloads?
Governance & Security (15 min): Design data governance covering: access control (who sees what), data quality checks in the pipeline, encryption, audit logging, and retention policies for sensor data.
Case Study Comparison (15 min): Compare your GreenEnergy design to the manufacturing case study from the chapter (Azure IoT predictive maintenance). What patterns are similar? What's different about energy vs. manufacturing IoT analytics?

Deliverables:

Architecture diagram, service selection table, cost optimization plan, governance document, case study comparison.

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