Building Scalable and Secure Industrial IoT Solutions Using Open Source

Manufacturing organizations implementing data infrastructure face decisions about technology platforms and architectural patterns. Open source options provide alternatives to proprietary industrial software, offering different trade-offs in terms of cost, flexibility, and vendor dependencies. Understanding these options enables informed decisions about data platform architecture.

Jeremy Theocharis, CTO and co-founder of United Manufacturing Hub, brings experience implementing data infrastructure using open source technologies. His work with manufacturing organizations demonstrates how open source components can address data integration, storage, and accessibility requirements in operational environments.

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IT and OT Integration Challenges

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The integration of information technology and operational technology systems presents challenges that are primarily organizational rather than technical. IT and OT evolved as separate domains with distinct priorities, tools, and practices. These groups developed different vocabularies, used different standards, and operated under different constraints.

IT organizations focus on standardization, security policies, and enterprise-wide systems. They manage networks, enforce access controls, and maintain data governance frameworks. OT organizations focus on production continuity, real-time control, and equipment reliability. They manage PLCs, SCADA systems, and production processes.

These different priorities create friction during integration projects. IT security policies may conflict with OT requirements for direct equipment access. IT's preference for cloud-based systems may conflict with OT's need for local control and data sovereignty. IT's standardized hardware platforms may not meet OT's requirements for industrial certifications and environmental specifications.

The technical challenges in IT/OT integration often stem from these organizational gaps. Projects stall not because protocols are incompatible, but because IT and OT groups have not established shared understanding about requirements, responsibilities, and constraints. Successful integration requires both groups to understand each other's priorities and develop shared approaches to data infrastructure.

Common integration friction points:

• Different security models between IT networks and OT networks
• Conflicting requirements for data location and access patterns
• Limited communication between IT and OT organizations
• Separate technology evaluation criteria and procurement processes

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Open Source Approach for Data Infrastructure

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Open source software provides manufacturing organizations with access to well-established technologies without vendor licensing constraints. Many industrial IoT implementations use adapted versions of technologies originally developed for IT applications—message brokers, time-series databases, container orchestration platforms.

The economics of open source differ from proprietary industrial software. Organizations can deploy open source components without per-device or per-connection licensing fees. This changes the cost structure for large-scale deployments where licensing costs would otherwise scale linearly with the number of data points or connected devices.

Open source adoption requires different organizational capabilities compared to proprietary solutions. Proprietary vendors typically provide integration services, technical support, and hosted deployment options. Open source implementations require internal expertise or system integrator partnerships for deployment, configuration, and maintenance.

The trade-off involves upfront integration effort versus long-term flexibility and cost. Proprietary solutions often provide faster initial deployment through vendor services and pre-configured systems. Open source solutions require more initial configuration but provide greater flexibility for customization and avoid vendor dependencies for long-term operation.

Considerations for open source adoption:

• Lower licensing costs offset by integration and maintenance requirements
• Access to standard IT tools and platforms rather than specialized industrial software
• Flexibility to customize components for specific requirements
• Requirement for technical expertise in deployment and configuration

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Unified Namespace Architecture Pattern

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The Unified Namespace concept describes an architectural pattern where all operational data flows through a central messaging system, creating a single logical namespace for data across the organization. Rather than point-to-point connections between systems, each system publishes data to the namespace and subscribes to data it needs.

This pattern uses publish-subscribe messaging, typically implemented with MQTT brokers. Production equipment publishes data to topics organized hierarchically—by site, area, production line, and equipment. Applications subscribe to relevant topics to receive data. Adding a new data consumer does not require reconfiguring data producers.

The Unified Namespace provides data context through topic structure. Instead of receiving datapoints with cryptic identifiers, systems receive data organized by its source location in the manufacturing hierarchy. The topic path itself provides metadata about what the data represents and where it originated.

Implementation requires standardizing on topic naming conventions and data formats across the organization. This standardization enables different systems to consume data without custom integration logic for each source-consumer pair. The namespace becomes the shared data contract between operational systems.

Unified Namespace characteristics:

• Centralized message broker provides single point of data publication and subscription
• Hierarchical topic structure organizes data by operational context
• Publish-subscribe pattern decouples data producers from consumers
• Standardized approach to data organization reduces integration complexity

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Microservices Architecture for Data Processing

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Modern data infrastructure increasingly uses microservices patterns where functionality is distributed across multiple small, specialized services rather than monolithic applications. Each microservice performs a specific function—protocol translation, data transformation, database writing, alerting, visualization.

This architectural approach provides several benefits for operational data systems. Individual microservices can be updated or replaced without affecting other components. New functionality can be added by deploying additional microservices that subscribe to relevant data topics. Failed microservices can be restarted without system-wide impact.

Containerization technologies like Docker and Kubernetes enable microservices deployment. Each microservice runs in a container with its dependencies isolated from other services. Container orchestration platforms manage deployment, scaling, and recovery of these services across available hardware.

The trade-off involves operational complexity. Managing numerous microservices requires understanding of container platforms, networking between services, and monitoring of distributed systems. Organizations need capabilities in these areas or must develop them during implementation.

Microservices architecture benefits:

• Independent deployment and updating of individual components
• Ability to scale specific functions based on load
• Fault isolation limits impact of individual service failures
• Flexibility to mix technologies appropriate for different functions

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Database Selection for Operational Data

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Selecting appropriate database technologies for operational data storage involves understanding different requirements compared to traditional enterprise databases. Operational data is typically time-series data—measurements, states, and events timestamped with their occurrence.

Traditional relational databases organize data in normalized tables with relationships between entities. This structure works well for transactional data but creates complexity for time-series queries. Retrieving a sensor's values over time requires joining multiple tables and filtering by timestamp, which impacts query performance at scale.

Time-series databases optimize for the access patterns common in operational data analysis. They store measurements indexed by timestamp and tags identifying the source. Queries for a sensor's values over time execute efficiently without joins. Aggregate functions like averages over time windows are built into the database query language.

However, time-series databases may not provide all functionality that operational users expect. Traditional historians provide features like data modeling according to ISA-95 hierarchies, pre-built visualization interfaces, and query tools designed for process engineers. Open source time-series databases provide the storage layer but may require additional tools for these higher-level functions.

The selection depends on who needs to access the data and for what purposes. If analytics teams will primarily access data through programmatic interfaces, time-series databases provide efficient storage and retrieval. If process engineers need direct database access for troubleshooting, additional tooling may be required to make time-series databases accessible to non-programmers.

Database technology considerations:

• Time-series databases optimize for operational data access patterns
• SQL databases provide broader tooling but require more complex queries for time-series data
• Historian products provide OT-friendly interfaces but may limit IT accessibility
• Hybrid approaches may serve different user groups through different interfaces

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Data Accessibility for Different User Groups

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Data infrastructure must serve different user groups with different technical capabilities and requirements. Process engineers need to query recent data for troubleshooting. Data scientists need bulk data access for model development. Analysts need flexible query capabilities for investigation.

Traditional historians were designed for process engineer workflows—browsing equipment hierarchies, selecting tags, and viewing trends. These interfaces work well for OT users but may not provide the programmatic access that data science teams need for extracting large datasets or integrating with analysis tools.

Modern time-series databases provide programmatic interfaces that data scientists can use effectively but may not provide the browsing and visualization interfaces that process engineers expect. This creates a situation where the database technology that works well for one user group does not serve another group effectively.

Solutions involve layering appropriate interfaces over the underlying data storage. Time-series databases can be accessed through SQL-compatible query layers that provide familiar interfaces for IT users. Visualization tools can provide browsing interfaces over time-series databases that work for OT users. Both groups access the same underlying data through interfaces appropriate for their workflows.

User group access requirements:

• Process engineers need browsing interfaces and trend visualization
• Data scientists need programmatic access for bulk data extraction
• IT teams need integration with standard monitoring and alerting tools
• Different interfaces can provide appropriate access to shared data storage

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Scalability and Maintainability Requirements

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Data infrastructure in manufacturing must handle increasing data volumes as more equipment is connected and higher-frequency sampling is implemented. Scalability involves both storage capacity for historical data and throughput for real-time data processing.

Open source technologies developed for IT applications often include built-in scalability mechanisms. Distributed databases can add storage nodes as data volume increases. Message brokers can distribute load across multiple servers. Container orchestration platforms can scale services based on resource utilization.

These scalability features align with IT best practices for distributed systems. However, they require operational expertise to configure and manage effectively. Organizations must develop capabilities in areas like cluster management, data replication strategies, and performance monitoring.

Maintainability involves routine operations like software updates, configuration changes, and troubleshooting. IT-standard tools provide established practices for logging, metrics collection, and alerting. These practices enable IT teams to maintain operational data systems using the same approaches they use for enterprise IT systems.

Infrastructure scalability considerations:

• Storage systems must accommodate increasing data retention requirements
• Message throughput must support adding more data sources
• Processing capacity must scale with real-time analysis requirements
• Operational practices must enable IT teams to maintain systems effectively

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Implementation Approach and Considerations

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Organizations implementing open source data infrastructure should consider their internal technical capabilities and long-term support requirements. Open source provides flexibility and cost benefits but requires expertise for effective deployment and operation.

Starting with well-documented, widely-adopted components reduces implementation risk. Technologies with active communities provide resources for troubleshooting and learning. Common platforms like PostgreSQL, TimescaleDB, and MQTT brokers have extensive documentation and community support.

Initial implementations should focus on establishing the core data flow from equipment to storage through standardized interfaces. This creates the foundation for adding additional capabilities incrementally. Starting with comprehensive functionality often leads to complex configurations that are difficult to troubleshoot and maintain.

Organizations should plan for developing internal expertise or establishing partnerships with system integrators experienced in open source industrial data platforms. The flexibility and cost benefits of open source depend on having the technical capabilities to effectively implement and maintain these systems.

Pilot projects provide opportunities to develop experience with open source platforms before committing to organization-wide deployments. These projects should include not just initial implementation but also operations over time to understand maintenance requirements and operational patterns.

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