DATATRONiQ Platform
Cloud analytics platform that orchestrates edge gateways, runs ML models, and publishes KPIs through REST and gRPC into MES and ERP.
§ Technical footprint
- Protocols
- OPC UA
- MQTT
- PROFINET
- EtherCAT
- REST · gRPC
- Deployment
- on-prem
- edge
- hybrid cloud
- air-gapped
- k8s · bare-metal
- Compliance
- TISAX*
- ISO 27001*
- GDPR-compliant
- Made in Germany
Versions · protocols · limits
- RUNTIME
- Kubernetes 1.29+
- Spark 3.5
- Flink 1.18
- STORAGE
- Postgres 16
- TimescaleDB 2.14
- S3-compatible object
- PROTOCOLS
- REST · gRPC · Kafka · Webhooks
- LATENCY
- p95 API read ≤ 120 ms · stream ≤ 5 s
- DEPLOY
- on-prem
- hybrid cloud
- air-gapped
- k8s · bare-metal
- COMPLIANCE
- TISAX*
- ISO 27001*
- GDPR
What it does.
- P01
Stream orchestration
Each ingest pipeline is a declarative resource. Schema, retention, and sinks are version-controlled; rollouts are zero-downtime. Models and dashboards depend on the same catalog — no drift between what the plant sends and what the analyst queries.
- P02
Model runtime
ONNX and PyTorch artifacts are promoted from the registry into live inference with a single commit. Shadow traffic, canary windows, and automatic rollback are the default — not a custom workflow.
- P03
Operator console
Role-scoped dashboards surface KPI trends, model drift, and open alerts side by side. Every chart is backed by a queryable time-series endpoint; Grafana and Power BI read the same API the UI reads.
- P04
Integration surface
Outbound hand-off to SAP, shop-floor MES, and third-party BI happens over REST, gRPC, Kafka topics, or signed webhooks. Mutual TLS everywhere; per-connector audit log.