Document maritime data synthesizer rationale

[irjudson]

Closes #16

Adds a comprehensive 'Why Maritime Data?' section to the README explaining the rationale for using maritime vessel tracking data as the test environment.

What's Covered

Characteristics that match real-world sync challenges:

• High volume and continuous flow (144K+ records/day)
• Temporal ordering constraints (chronological requirement)
• Geospatial and complex schema (lat/lon, metadata, multi-table)
• Velocity flexibility (slow to fast data streams)

Production use case representation:

• IoT & Telemetry (sensor streams)
• Event Tracking (user activity, logs)
• Time-Series Data (financial, monitoring)
• Geospatial Analysis (fleet management, delivery)

Benefits for plugin testing:

• Realistic scale, reproducible patterns
• Multi-table testing capabilities
• Global distribution patterns
• Physics-based validation

When to use maritime data vs custom data:

• Good fit for time-series, distributed workloads, multi-table testing
• Consider custom data for domain-specific constraints

Why This Matters

Users evaluating this plugin can now understand:

• Why we chose maritime data (not arbitrary)
• How it represents their use case (IoT, events, time-series)
• When to use it vs their own test data
• What characteristics make good test data for sync plugins