iot database vs google database
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1,111,111 TRP = 11,111 USD
1,111,111 TRP = 11,111 USD
1,111,111 TRP = 11,111 USD
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IoT Databases
Designed for edge computing and real-time processing of sensor data. Examples include InfluxDB, TimescaleDB, and MongoDB IoT.
Strengths:
Optimized for time-series data (e.g., sensor logs).
Lightweight, scalable for distributed systems.
Low-latency analytics (e.g., predictive maintenance).
Limitations:
Limited transactional support vs. traditional databases.
Google Databases
Cloud-native solutions like Firestore, BigQuery, and Cloud Spanner.
Strengths:
Global scalability and serverless architecture.
Strong ACID compliance (Spanner) and AI/ML integration (BigQuery).
Managed security and multi-region redundancy.
Limitations:
Higher cost for large-scale IoT deployments.
Latency issues if edge processing isn’t paired with Cloud IoT Core.
Key Difference
IoT DBs prioritize edge efficiency; Google DBs excel in cloud-scale analytics.
Use Case: IoT databases for real-time edge data, Google databases for unified cloud storage and AI-driven insights.