Data Pipelines for Manufacturing: Telemetry, IoT, MES and SCADA
Manufacturing data pipelines unify plant telemetry (PLCs, SCADA, historians), MES production data, ERP business data, IoT sensor streams, and quality data into unified analytical and AI-ready infrastructure. BearPlex builds these systems with the operational rigor that plant-floor reliability requires: edge + cloud hybrid architecture, integration with industrial protocols (OPC UA, MQTT, Modbus, EtherNet/IP), and design for plant-team operational ownership. We've shipped pipelines handling billions of telemetry events per month with deep integration to Siemens, Rockwell, and GE Digital platforms.
Why Data Pipelines & MLOps matters in Manufacturing & Industrial
Manufacturing has exceptional operational data (historians collect billions of telemetry events per day, MES tracks every production order, SCADA logs every operator action), but the data is largely siloed within operational systems and unavailable for analytics, AI, or cross-functional use. The opportunity from unifying this is large: predictive maintenance, quality optimization, energy reduction, throughput improvement, and increasingly AI features all depend on accessible operational data. The constraints that shape engagements: (1) Industrial protocols (OPC UA, MQTT, Modbus, EtherNet/IP) aren't standard cloud-software integrations; (2) Plant networks are isolated by design (ISA/IEC 62443): connectivity to cloud requires careful security architecture; (3) Edge processing is needed for latency-critical or bandwidth-limited use cases; (4) Brownfield realities: most plants have decade-old equipment with limited connectivity that requires careful integration; (5) Operational ownership matters: plant teams must be able to operate the system, not require permanent vendor support. The pipelines that work in manufacturing are designed for these realities.
Typical data pipelines & mlops use cases in manufacturing & industrial
| Application | Description | Timeline | Tech stack |
|---|---|---|---|
| Plant telemetry ingestion (historian integration) | Pipeline ingesting telemetry from OSI PI, Wonderware, and GE Proficy historians and PLCs into modern analytics. Handles billions of tag readings per day. | 12-18 weeks | OPC UA / MQTT integration · Edge gateway processing · Snowflake or InfluxDB for time-series · ISA/IEC 62443-compliant network architecture |
| MES and production data pipeline | Pipeline integrating MES (Siemens, Rockwell, GE) order and quality data with telemetry. Powers production analytics, OEE measurement, root-cause analysis. | 12-18 weeks | MES vendor APIs · Snowflake / Databricks · dbt · Production analytics dashboards |
| Edge data processing infrastructure | Edge computing for latency-critical processing: real-time anomaly detection, in-line quality, control loop integration. Edge nodes with cloud aggregation. | 14-20 weeks | NVIDIA Jetson / industrial PCs · MQTT / Kafka edge brokers · Time-series databases (InfluxDB, TimescaleDB) · Cloud aggregation |
| IoT sensor pipeline (greenfield) | End-to-end IoT pipeline for new sensor deployments: device management, secure ingestion, time-series storage, analytics. Beyond existing PLC and SCADA coverage. | 16-22 weeks | AWS IoT / Azure IoT Hub · MQTT · Time-series databases · Device management infrastructure |
| AI-ready feature pipeline for manufacturing ML | Curated feature pipeline supporting manufacturing ML: predictive maintenance features, quality prediction features, process optimization features. | 12-16 weeks | Tecton or custom feature pipeline · Time-series feature engineering · Online store for real-time inference |
What we've learned deploying data pipelines & mlops in manufacturing & industrial
Three patterns from BearPlex manufacturing data pipeline engagements: (1) Network architecture is the binding constraint; plant networks are isolated for safety and security reasons (ISA/IEC 62443), and connectivity to cloud requires careful security design; we work with the customer's IT and OT teams from day one rather than assuming cloud-software network patterns; (2) Edge vs cloud decisions depend on use case: real-time control needs edge; analytics works fine in cloud with appropriate ingestion latency; we design hybrid architecture explicitly; (3) Plant team ownership is required for sustainability: manufacturing data pipelines that depend on permanent vendor support fail when vendor relationships change; we design for plant team operational ownership with documentation, training, and runbooks. The clients who succeed treat manufacturing data pipelines as plant operations infrastructure, not a standalone software project.
Manufacturing & Industrial compliance considerations
Manufacturing data pipelines must respect: ISA/IEC 62443 industrial cybersecurity standard for any pipeline connected to control systems; FDA 21 CFR Part 11 for pharmaceutical and medical device manufacturing data systems; sector-specific quality frameworks (ISO 9001, ISO 13485, AS9100, IATF 16949); export controls (ITAR / EAR) for defense / dual-use manufacturing; data residency requirements for international manufacturing operations. For environmental data (EPA reporting, energy management), additional regulatory frameworks apply. BearPlex designs around these from day one: segmented network architecture, audit logging, validated data flows for regulated manufacturing.
Common questions
ISA/IEC 62443-compliant architecture: segmented networks with explicit data flow controls, jump servers / DMZ for cross-zone communication, encrypted data flows. We work with the customer's IT and OT teams to design network architecture that satisfies both operational reliability and security requirements.
Yes: common requirement. NVIDIA Jetson / industrial PC edge nodes for vision-heavy use cases or compute-heavy processing. MQTT / Kafka brokers at the edge for event aggregation. We design hybrid architecture (edge + cloud) explicitly per use case requirements.
Pragmatically. We meet the plant where it is. For plants with modern PLC fleets and OPC UA support, we use standard industrial protocols. For plants with older equipment, we use protocol gateways or work with the equipment via legacy protocols. For equipment with no connectivity, we sometimes recommend retrofitting (smart sensors, edge data acquisition) but only when it's clearly justified.
$200K-$700K for a 12-22 week engagement depending on scope, edge requirements, and integration complexity. Includes: architecture, historian / MES / SCADA integration, edge processing infrastructure, cloud data warehouse, transformation pipelines, observability, training for plant team, and 60-day handover. Edge hardware separate when applicable.
Yes: common engagement type. For pharmaceutical and medical device manufacturing, we work within FDA 21 CFR Part 11 frameworks. For aerospace and automotive, we provide documented validation matching the quality management system requirements.
Yes: designed for. Standard pattern: pipelines designed for plant team ownership with comprehensive documentation, runbooks, training, and observability that plant ops can use without requiring vendor support. We support post-handover with retainer arrangements when the client wants ongoing engineering capacity, but the system is designed to operate independently.
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