1264 Data Storage and Databases
1264.1 Overview
IoT systems generate diverse data types requiring different storage strategies. This comprehensive chapter series covers database selection, distributed systems trade-offs, time-series optimization, data quality monitoring, sharding strategies, and complete worked examples.
This chapter has been split into focused topics for better learning:
1264.1.1 Chapter Series
| Chapter | Topics Covered | Approx Words |
|---|---|---|
| Database Selection Framework | Choosing SQL vs NoSQL vs time-series, decision framework, real-world examples | ~4,500 |
| CAP Theorem and Database Categories | Distributed systems, consistency vs availability, database trade-offs | ~3,800 |
| Time-Series Databases | TimescaleDB, InfluxDB, hypertables, compression, retention policies | ~3,200 |
| Data Quality Monitoring | Quality dimensions, validation, monitoring dashboards, handling bad data | ~3,500 |
| Sharding Strategies | Horizontal scaling, time vs device vs hybrid sharding, implementation | ~2,800 |
| Worked Examples | Fleet management (10K vehicles), smart city data lake (50K sensors) | ~4,200 |
1264.1.2 Quick Start Guide
New to databases? Start with Database Selection Framework
Building for scale? Read CAP Theorem for distributed systems
Working with sensors? Jump to Time-Series Databases
Production systems? See Data Quality Monitoring
Massive scale? Learn Sharding Strategies
Need examples? Check Worked Examples
1264.2 Data Storage and Databases
This section provides a stable anchor for cross-references to storage and database concepts across the book.
1264.3 What You’ll Learn
This chapter series covers:
- Database Selection: Match database type to data characteristics and access patterns
- Distributed Systems: Understand CAP theorem trade-offs for IoT deployments
- Time-Series Optimization: Implement efficient storage for sensor telemetry
- Data Quality: Monitor and validate data quality in production systems
- Horizontal Scaling: Design sharding strategies for massive data volumes
- Complete Examples: Learn from real-world fleet management and smart city architectures
1264.4 Key Takeaways
- Use relational databases for device metadata and user accounts
- Use time-series databases for sensor telemetry (10-100x faster than generic SQL)
- Use NoSQL databases for flexible schemas and high write throughput
- Implement multi-tier storage (hot/warm/cold) for 80-95% cost reduction
- Apply CAP theorem to choose consistency vs availability for different data types
- Design hybrid sharding (device + time) for balanced write/query performance
1264.5 Where to Start
Begin with Database Selection Framework to understand how to choose the right database technology for your IoT application.