Module 8: Case Studies
Overview
This module presents detailed case studies from various industries, demonstrating how to apply the concepts learned in previous modules to real-world scenarios. Each case study includes architecture design, technology selection, and cost optimization strategies.
Case Studies
| Case Study | Industry | Key Challenges | Scale |
|---|---|---|---|
| FinTech Fraud Detection | Financial Services | Real-time ML, regulatory compliance | 10TB/day |
| Healthcare Real-time HIPAA | Healthcare | HIPAA compliance, real-time analytics | 1TB/day |
| E-commerce Personalization | Retail | Real-time recommendations | 100TB/day |
| AdTech Clickstream | Advertising | High-volume event processing | 1PB/day |
| IoT Manufacturing | Manufacturing | IoT data, predictive maintenance | 100TB/day |
| BFSI Banking Finance | Banking | Regulatory reporting, risk analytics | 50TB/day |
Case Study Template
Each case study includes:
- Business Context: Industry, company type, requirements
- Data Characteristics: Volume, velocity, variety, veracity
- Architecture Design: End-to-end system design
- Technology Selection: Rationale for each technology choice
- Cost Optimization: Specific strategies and savings
- SLA/SLO Requirements: Availability, latency, completeness
- Failure Modes: What can go wrong and mitigation
- Migration Strategy: How to get from current to target state
- Architecture Diagram: Mermaid visualization
- Key Takeaways: Principal-level lessons
Sample Case Study: E-commerce Personalization
Business Context
Company: Large e-commerce platform (10M daily active users)
Challenge: Deliver personalized product recommendations in real-time to increase conversion rates.
Requirements:
- Latency: < 100ms for recommendation API
- Freshness: User behavior updates in < 5 seconds
- Availability: 99.9% SLA
- Scale: 1M requests/second peak
Architecture Overview
Technology Selection
| Component | Technology | Rationale |
|---|---|---|
| Streaming | Kafka | Proven at scale, exactly-once semantics |
| Processing | Flink | Real-time, state management, windowing |
| Feature Store | Feast | Open source, offline/online stores |
| Model Serving | TensorFlow Serving | Low latency, batch/real-time |
| Real-time Analytics | ClickHouse | Fast ingest, excellent compression |
| Data Lake | S3 + Delta | ACID, time travel, cost optimization |
Cost Optimization Strategies
-
Streaming:
- Use spot instances for Flink (60-80% savings)
- Right-sized task managers based on state size
- Optimize state backend (RocksDB tuning)
-
Storage:
- Tiered storage (S3 Standard → IA → Glacier)
- Delta Lake compaction (OPTIMIZE every 4 hours)
- Z-Ordering on user_id, timestamp (5-10x query improvement)
-
Feature Store:
- Online store: Redis cluster (spot instances)
- Offline store: S3 Parquet with ZSTD
- TTL for expired features (automatic cleanup)
-
Model Serving:
- Auto-scaling based on request volume
- Model versioning (canary deployments)
- Batch inference for cold start
Total Monthly Cost:
- Before optimization: $150K/month
- After optimization: $85K/month (43% savings)
Case Study Comparison
| Case Study | Primary Challenge | Key Technology | Cost Savings |
|---|---|---|---|
| FinTech Fraud | Real-time ML | Flink + Feature Store | 40% |
| Healthcare HIPAA | Compliance | Encryption + Governance | 25% |
| E-commerce | Low latency | ClickHouse + Redis | 43% |
| AdTech | High volume | Kafka + Spark | 50% |
| IoT | Edge processing | Kinesis + Greengrass | 35% |
| BFSI | Regulatory | Delta Lake + dbt | 30% |
Architecture Patterns by Industry
FinTech
Healthcare
Learning Objectives
After completing this module, you will:
- Apply concepts to real scenarios: End-to-end architecture design
- Make technology decisions: Rationale for each choice
- Optimize costs: Specific strategies per industry
- Design for scale: TB/PB scale considerations
- Handle failure modes: Resilience patterns
- Plan migrations: From current to target state
Module Dependencies
This module synthesizes all previous modules into real-world scenarios.
How to Use Case Studies
- Before interviews: Study each case study end-to-end
- Practice explaining: Be able to describe the architecture in 10 minutes
- Focus on trade-offs: Why X instead of Y?
- Highlight cost: Every decision has cost implications
- Discuss failure modes: What happens when X fails?
Next Steps
- Study FinTech Fraud Detection
- Review AdTech Clickstream for high-volume patterns
- Practice explaining architecture for your industry
Estimated Time to Complete Module 8: 8-10 hours