1275  Data Storage Overview

Learning Objectives

After completing this chapter series, you will be able to:

  • Select appropriate database types for different IoT use cases
  • Understand the CAP theorem and its implications for IoT systems
  • Implement relational, NoSQL, and time-series databases for IoT data
  • Design data partitioning and sharding strategies
  • Implement data quality monitoring and retention policies
  • Optimize storage costs through tiered architectures
TipMVU: Minimum Viable Understanding

Core concept: Database selection depends on your data shape (relational vs. document vs. time-series) and the CAP theorem trade-offs between consistency, availability, and partition tolerance. Why it matters: The wrong database choice leads to 10x higher costs, query timeouts, and scaling nightmares; the right choice enables real-time analytics at IoT scale. Key takeaway: Use relational databases for transactional data with relationships, time-series databases for sensor readings, and document stores for flexible device metadata–then implement multi-tier retention (hot/warm/cold) to control storage costs.

1275.1 Introduction

IoT systems generate diverse data types requiring different storage strategies. Sensor readings demand time-series optimization, device metadata needs relational structure, and multimedia content requires object storage. Choosing the right database technology is crucial for performance, scalability, and cost-effectiveness.

This chapter series covers all aspects of IoT data storage:

1275.1.1 Chapter Guide

Chapter Focus Best For
Database Selection Framework Choosing the right database type Starting a new IoT project
CAP Theorem and Database Categories Distributed systems trade-offs Designing for scale and reliability
Time-Series Databases TimescaleDB, InfluxDB optimization Sensor data storage
Data Quality Monitoring Quality metrics and validation Production systems
Sharding Strategies Horizontal scaling patterns Large-scale deployments
Worked Examples Fleet management, data lake design Learning by example

1275.1.2 IoT Storage Challenges

IoT systems face unique storage challenges:

  • Scale: Billions of devices, petabytes of data
  • Velocity: High-frequency writes (1000s/second per device)
  • Variety: Structured, semi-structured, unstructured data
  • Retention: Long-term storage (years) vs short-term (hours)
  • Cost: Storage costs can exceed compute costs
  • Access patterns: Write-heavy, time-range queries

1275.1.3 The Three Main Database Types for IoT

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flowchart LR
    IoT[IoT Data] --> Choice{What Type<br/>of Data?}

    Choice -->|Fixed Schema<br/>Relationships| Relational[(Relational DB<br/>PostgreSQL, MySQL)]
    Choice -->|Flexible Schema<br/>JSON Documents| NoSQL[(NoSQL DB<br/>MongoDB, Redis)]
    Choice -->|Time-Stamped<br/>Sensor Readings| TimeSeries[(Time-Series DB<br/>InfluxDB, TimescaleDB)]

    Relational --> R1[Device Metadata<br/>User Accounts]
    NoSQL --> N1[Event Logs<br/>Configuration Data]
    TimeSeries --> T1[Sensor Readings<br/>Metrics Over Time]

    style IoT fill:#E67E22,stroke:#2C3E50,color:#fff
    style Choice fill:#7F8C8D,stroke:#2C3E50,color:#fff
    style Relational fill:#2C3E50,stroke:#16A085,color:#fff
    style NoSQL fill:#16A085,stroke:#2C3E50,color:#fff
    style TimeSeries fill:#2C3E50,stroke:#16A085,color:#fff

Figure 1275.1: Database Type Selection: Choose relational for structured metadata, NoSQL for flexible documents, or time-series for sensor readings based on your data characteristics.
Type Analogy Best For Examples
Relational Spreadsheet with strict columns Device metadata, user accounts PostgreSQL, MySQL
NoSQL Flexible JSON file Event logs, config data MongoDB, Redis
Time-Series Optimized data log Sensor readings, metrics InfluxDB, TimescaleDB

1275.1.4 Quick Reference: Database Comparison

Database Type Best For Scalability Query Complexity Write Speed
Relational (SQL) Structured data, ACID Vertical High (SQL) Medium
Document (NoSQL) Semi-structured Horizontal Medium High
Key-Value Simple lookups Horizontal Low Very High
Time-Series Time-stamped data Horizontal Medium Very High
Graph Relationships Horizontal High (traversals) Medium

1275.2 Where to Start

New to databases? Start with Database Selection Framework to understand how to choose the right database for your IoT use case.

Building for scale? Read CAP Theorem and Database Categories to understand distributed systems trade-offs.

Working with sensor data? Jump to Time-Series Databases for optimization techniques.

Learning by example? Check Worked Examples for fleet management and smart city data lake designs.

1275.3 What’s Next

Continue with Database Selection Framework to learn how to choose the right database technology for your IoT application.