Industrial IoT & Smart Factory

10KHz sensors tell the plant what is happening. Agent memory helps the plant remember what worked last time. ZeptoDB combines edge time-series ingestion with maintenance context, cache lookup, and replayable agent decisions.

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Why manufacturers choose ZeptoDB

10KHz+ ingestion per sensor

Vibration, current, pressure, and temperature at the rate the physics demand. Lock-free pipeline with no jitter on the hot path.

Real-time analytics in SQL

Rolling averages, SPC, anomaly detection, threshold alerts — standard SQL window functions, no UDFs to maintain.

Edge to cloud

ARM Graviton on the line, multi-node cluster in the plant, Parquet HDB on S3 for long-tail history. Same binary, same SQL.

Industrial protocols

OPC-UA and MQTT connectors for PLC, SCADA, and brownfield sensor buses. Built-in Kafka consumer for existing streaming infra.

Maintenance memory

Keep prior diagnoses, operator notes, work orders, model calls, and tool outputs searchable by machine, session, and incident.

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Where it fits

Predictive maintenance

• Ingest vibration, temperature, current, and oil-debris sensors at 10KHz+
• Rolling statistics (EMA, stddev, percentiles) for drift and early-warning signatures
• ASOF JOIN to correlate sensor anomalies with maintenance events and work orders

Quality control & SPC

• Real-time statistical process control with window functions
• Detect out-of-spec measurements within microseconds of the sample hitting the bus
• Historical analysis from Parquet HDB for root-cause investigation and yield review

Energy & OEE

• Sub-second power consumption aggregation across line, cell, and facility
• xbar time bucketing for shift, hourly, and daily OEE and energy-per-unit reports
• Peak-demand detection and load balancing analytics in standard SQL

Digital twin & process optimization

• High-fidelity historian for simulation calibration and what-if analysis
• Join live sensor streams with setpoints and batch records
• Feed production ML models with the same zero-copy path used in research

Industrial agent workflows

• Retrieve similar failures before recommending a maintenance action
• Pair live alarms with prior work orders, operator notes, and pinned safety procedures
• Cache repeated explanations for common machine states
• Store agent decisions and tool calls as AgentOps telemetry for audit and improvement

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Example: vibration anomaly detection

-- Detect anomalous vibration readings using rolling statistics
SELECT
    sensor_id,
    ts,
    vibration,
    ema(vibration, 100) AS ema_100,
    vibration - ema(vibration, 100) AS deviation
FROM sensor_readings
WHERE
    ts > now() - interval '5 minutes'
    AND abs(vibration - ema(vibration, 100)) > 3 * stddev(vibration)
        OVER (PARTITION BY sensor_id ORDER BY ts ROWS 1000 PRECEDING)
ORDER BY deviation DESC

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Deployment options

Environment	Configuration
Line / cell edge	Single node, ARM Graviton, 16 GB RAM, local NVMe
Plant cluster	3-node cluster, 10 GbE, Parquet HDB on NAS
Enterprise historian	Multi-node EKS, Parquet HDB on S3, cross-site replication
Hybrid	Edge for real-time, cloud sync for cross-plant analytics and ML

See Production Deployment for reference architectures.

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Why this matters commercially

Industrial data platforms typically stitch together a historian, a stream processor, a vector store, a feature store, and a warehouse. ZeptoDB replaces the hot path with one model for live facts and agent context, from a ruggedized edge node to the cloud. Fewer integrations, less bespoke glue, and a clearer path from pilot line to production agent.

Talk to us about your plant or product: Contact Sales →