An exabyte-scale, edge-to-cloud data platform for the diverse data needs of Industrial-IoT enterprise applications. Large-scale data ingestion and analysis is a multi-step journey. The BEARTELL Pole Position Data Platform ingests, stores, and manages data on a massive scale to make analytical compute techniques and storage instantly accessible across on-premises, edge, and hybrid architectures.
Real-time telemetry streams monitored at sub-second frequency, with rule-based alarms and predictive maintenance routines executed continuously on edge nodes.
• Wide-Ranging Field Ingestion: Supports high-speed telemetry from OT/IT devices, PLCs, DCS, and SCADA via OPC-UA, Modbus TCP/RTU, Siemens S7, MQTT/Sparkplug B, Kepware, and AVEVA PI.
• Embedded ChronoLake Lakehouse: Embedded DuckDB analytical engine with ZSTD Parquet archives delivers sub-millisecond SQL queries over billions of telemetry rows with up to 85% disk storage savings.
• 5 Embedded Northbound Servers: Built-in PGWire (:15432 - Grafana, PowerBI, Tableau), HTTP REST & SSE (:8090), OPC-UA (:14840), Modbus Slave (:5502), and MQTT Broker (:11883) serve data without intermediary middleware.
• Autonomous Auto-Pilot & Safety: Inspects field telemetry in 500ms-2000ms cycles, triggering automated DAG safeguard routines on anomalies with hardware-level LOTO (Lockout/Tagout) physical protection.
• 100% Air-Gap Sovereign Data: Operates entirely within air-gapped industrial facilities without sending a single byte to external clouds, guaranteeing complete KVKK and data sovereignty.
• Ultra-Lean Footprint: Runs smoothly under 1.5 GB RAM on industrial IPCs, Raspberry Pi, ARM64 edge nodes, and central server clusters.
Enterprises are actively building strategies to maximize the value of industrial telemetry and artificial intelligence. The B.E.A.R.TELL Pole Position Data Platform is built on three core pillars:
1. System Connectivity & Ingestion: Connects to DCS, SCADA, PLC, and historians while fusing telemetry with ERP and MES systems into an embedded lakehouse.
2. Predictive Analytics & Auto-Pilot: Provides forward-looking predictions, automated DAG protection flows, and machine learning inference directly on data streams.
3. Real-Time Monitoring & Rule-Based Alerts: Observes critical equipment health in real time, generates time-series visualizations, and produces instant operational notifications.
