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Version: 3.0 (next)

Line-side edge

One MaestroHub binary on an industrial PC next to the line reads the machines, keeps the plant's Unified Namespace and feeds MES, ERP and the cloud. This is the smallest deployment and the usual first one.

Scenario
1 of 5
Shape
Tier 1 · one machine per plant
Applies to
3.0

Choose it when you have one plant on one network, no Kubernetes skills on site, and Windows or Linux industrial PCs.

Line-side edgeTier 1 · one deployment
  • MaestroHub
  • Reads machines
  • Data
  • Buffered on disk
  • Management
  • Your tooling
L5EnterpriseL4BusinessL3.5Plant DMZL3OperationsL2SupervisoryL1ControlPlantone network, one MaestroHubStore-and-forward: queued on the sender's disk while the far side is downStore-and-forward: queued on the sender's disk while the far side is downCloud analyticsEvent Hubs · Snowflake · S3MESwork orders · OEESAP ERPorders · goods receiptMaestroHub · Tier 1One process, every module insideConnectors · pipelines · UNS · historianEmbedded NATS · MQTT broker · Pebble · SQLiteNative service: systemd or WindowsIndustrial PC · 8 vCPU · 16 GB · 200 GB SSDSCADA / HMIsubscribes to UNS topicsS7-1500 PLCS7DrivesModbus TCPCNCOPC UAVision stationRESTpoll · subscribeMQTT :1883REST · SQLSAP IDocEvent Hubs

What you install

LevelComponentHow it runsRecommended hardware
L3MaestroHub, Tier 1One binary as a native service (systemd or a Windows service)8 vCPU, 16 GB RAM, 200 GB SSD

When something fails

What failsWhat happens
The MaestroHub processThe service manager restarts it after 5 seconds. Buffered writes survive the restart, because the buffers are files on disk.
The uplink to ERP or the cloudReading the machines and serving the floor carry on. Writes that fail wait in their Store & Forward buffers. When the 50 GiB disk pool or a buffer's limit is reached, the buffer's drop policy applies (by default, drop the oldest).
The PCReading stops until the PC is replaced and its configuration restored. Tier 1 has no standby.

Limits. There is no high availability: one machine is one point of failure. Each further plant is a separate deployment; scenarios 4 and 5 show how to run many.


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