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Scalable Historian

Industrial-Grade Data Storage for
IoT Time-Series Analytics

Store billions of time-series data points from sensors, PLCs, and devices. Query years of production history in seconds with columnar storage optimized for industrial analytics.

Why Proxus Data Storage?

Traditional databases struggle with high-frequency sensor data. Proxus uses a columnar time-series engine purpose-built for industrial telemetry—handling millions of writes per second while keeping query latency low.

  • Sub-second queries—analyze years of production data without waiting.
  • 10:1 compression—store more history with 90% less disk space.
  • Automatic retention—define TTL policies to purge old data without manual cleanup.
  • Built-in aggregations—pre-compute averages, minimums, and maximums at ingest time.

What you can store

  • Device tags and sensor readings (temperature, pressure, flow)
  • Production metrics (OEE, cycle time, downtime events)
  • Energy consumption (kWh, steam, water, gas)
  • Quality measurements (dimensions, weights, defects)
  • Equipment health (vibration, runtime hours, alarms)
All indexed by timestamp and tag path for instant drill-down.

Storage Architecture

Data flows from edge gateways through the unified namespace into optimized storage partitions. Queries span hot and cold tiers seamlessly—recent data stays in memory while historical data lives on disk.

Batch writes—edge nodes buffer and send data in optimized chunks.
Columnar format—compress numeric columns and skip irrelevant data during scans.
Index by time and tag—filter by date range and device path in milliseconds.

Example retention policy

Raw data: 30 days
1-min aggregates: 1 year
1-hour aggregates: 5 years
Daily summaries: Forever

Balance storage cost and query speed by defining granularity tiers.

Query Performance

Dashboards, reports, and analytics tools query the same storage layer. No ETL pipelines or duplicate warehouses—just fast reads from a single source of truth.

  • Real-time + historical—query live UNS cache and long-term storage in one request.
  • Tag-based filtering—select only the sensors and lines you need.
  • SQL-compatible—use familiar aggregations and JOINs for complex analytics.

Use cases

  • Trend analysis across production shifts
  • Root cause investigation for downtime events
  • Energy audits and ISO 50001 reporting
  • Quality correlation studies (process vs. outcome)
  • Predictive maintenance feature engineering

Integration & Compliance

Data storage integrates seamlessly with the rest of Proxus. Dashboards subscribe to live data while pulling historical context. Rule engines trigger on patterns across time windows. IT systems export archives for regulatory audits.

  • Audit trails—track who queried what and when.
  • Role-based access—scope queries by user, site, or line.
  • Export formats—CSV, Parquet, or direct API access for BI tools.

Compliance ready

  • 21 CFR Part 11 (pharma records)
  • ISO 50001 (energy management)
  • FDA validation (batch genealogy)
  • GDPR (data retention and deletion)

FAQ

Common questions on storage capacity, query limits, and retention policies.

How much data can I store?

Billions of rows per table. Storage scales with disk; typical deployments handle 10+ years of sensor history.

What's the write throughput?

Millions of rows per second per node. Batch inserts from edge gateways keep overhead low.

Can I delete old data automatically?

Yes. Define TTL (time-to-live) policies by table or partition to drop data after retention windows expire.

Does it integrate with external BI tools?

Yes. Export via REST API, ODBC, or direct database connectors for Power BI, Tableau, and Grafana.

Ready to store industrial data at scale?

From sensor telemetry to production analytics—store it once, query it fast, keep it compliant.

Explore Platform Overview See How UNS Feeds Storage