Real-time Analytics & IoT

Process millions of events per second with time-series and graph analytics

The Challenge

IoT deployments generate massive volumes of time-series data from sensors, devices, and systems. Organizations need to:

  • Ingest and process millions of events per second
  • Analyze device relationships and dependencies in real-time
  • Detect anomalies and trigger immediate actions
  • Correlate events across device hierarchies
  • Maintain both real-time and historical analytics

Traditional time-series databases lack graph capabilities, while graph databases struggle with high-velocity time-series ingestion. This forces complex multi-database architectures.

Multi-Model Solution

ArcadeDB combines time-series, graph, and document models:

  • Time-series: Efficient storage for sensor readings
  • Graph: Device topology and relationships
  • Documents: Device metadata and configurations
  • Single Query: Analyze all three in one operation

Real-World Success

"Our smart building platform monitors 50,000+ sensors across industrial facilities. Before ArcadeDB, we used separate databases for time-series data and device relationships, causing significant complexity and latency. Now we ingest 2M+ events per second and can instantly correlate anomalies across related devices. Maintenance predictions improved by 45%."

— CTO, Industrial IoT Solutions Provider
(Company identity protected by NDA)

Key Results:

  • 2M+ events/sec ingestion rate
  • 45% improvement in predictive maintenance accuracy
  • 70% reduction in infrastructure complexity
  • Real-time alerting in <100ms

Technical Capabilities

ArcadeDB allows you to traverse device networks to find correlated anomalies, analyzing time-series measurements across connected devices with a single query. By combining device graph topology with time-series analysis, you can identify cascading failures and correlated issues without complex joins or data movement.

Ready to Scale Your IoT Analytics?

Start exploring ArcadeDB today and see how our multi-model approach can handle your real-time analytics at scale.

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