Simplifying Industrial Data with Barbara: A Unified Namespace Use Case for Food & Beverage

In thispost, we walk you through how Barbara’s edge orchestration platform can be used to deploy a robust UNS solution in a Food & Beverage plant, from data acquisition to real-time analytics and future-ready AI integration.

Technology
Written by:
Enrique Ramírez

Introduction

In today’s highly automated industrial environments, the ability to seamlessly collect, process, and integrate data from diverse machines and systems is key to achieving operational excellence. This is particularly relevant in the Food & Beverage sector, where real-time visibility, traceability, and agility are non-negotiable. Enter the Unified Namespace (UNS), a powerful data architecture paradigm that Barbara makes incredibly easy to implement and scale.

What is a Unfied Namespace (UNS)?

A UNS is a real-time, structured data layer built on top of protocols like MQTT and Sparkplug. It acts as the single source of truth, where data from industrial assets, no matter the vendor or protocol , is published in a standardized format. This enables enterprise-wide data visibility, making integration with SCADA, MES, BI, and AI systems a breeze.

Barbara’s platform makes deploying a UNS straightforward, even in complex industrial setups, thanks to its edge-native architecture, containerized app deployment, and built-in support for industrial protocols.

Use Case Overview: A Multi-Level Food & Beverage Architecture

In this example, we implement a UNS across a Food & Beverage company with multiple factory sites.

Proposed UNS Architecture

Let’s break down the architecture:

Levels defined

There are 3 levels:

  • Enterprise Level:  Central hub for business intelligence and analytics. Optionally deployed in the cloud or on-prem data center. 
  • Site Level: An edge node in each plant aggregates data from production lines for storage, visualization, and analytics.
  • Line Level: Each production line (in this case, 2 per plant) has its own edge node connected to industrial machinery.

Machines per Production Line

In this case, we will consider the following machines in the 2 production lines of our plant.

Production Line 1:

  • Dough Mixer (MX-5000): Mixes ingredients to form dough.
  • Conveyor Belt (CB-200): Transfers dough through the line.
  • Oven (OVN-750): Bakes products at controlled temperatures.
  • Cooling Unit (CL-300): Lowers product temperature before packaging.
  • Packaging Machine (PKG-400): Seals and labels finished goods.

Production Line 2:

  • Liquid Mixer (LM-8000): Blends liquid ingredients for beverages.
  • Pasteurizer (PST-600): Heats liquids to kill bacteria.
  • Bottling System (BTL-900): Fills and caps bottles.
  • Labeling Machine (LBL-500): Applies product labels.
  • Palletizer (PLT-700): Stacks products for shipping.

The edge nodes located at the production lines level must gather data from all the machines defined above.

Data Acquisition: Parameters & Data Types

The machines in each production line generate key process data, categorized as follows:

UNS Standardized Data Structure 

The UNS follows a hierarchical MQTT topic structure, ensuring organized data exchange

  • Factory/Line 1 Topics
    • Barbara/Madrid_01/kitchen/line1/DoughMixer_MX-5000
    • Barbara/Madrid_01/kitchen/line1/ConveyorBelt_CB-200
    • Barbara/Madrid_01/kitchen/line1/Oven_OVN-750
    • Barbara/Madrid_01/kitchen/line1/CoolingUnit_CL-300
    • Barbara/Madrid_01/packaging/line1/PackagingMachine_PKG-400
  • Factory/Line 2 Topics
    • Barbara/Madrid_01/kitchen/line2/LiquidMixer_LM-8000
    • Barbara/Madrid_01/kitchen/line2/Pasteurizer_PST-600
    • Barbara/Madrid_01/packaging/line2/BottlingSystem_BTL-900
    • Barbara/Madrid_01/packaging/line2/LabellingMachine_LBL-500
    • Barbara/Madrid_01/packaging/line2/Palletizer_PLT-700

Capture and publish

OPC UA publishes the following information obtained from the device MX-5000:

Barbara/Madrid_01/kitchen/line1/DoughMixer_MX-5000

{

    "deviceDisplayName": "DoughMixer_MX-5000",

    "data": {

        "MotorSpeed": 1932,

        "VibrationLevel": 2.259999990463257,

        "EnergyConsumption": 0.43700000643730164,

        "AlarmStatus": false,

        "FaultCode": "",

        "FaultDescription": ""

    },

    "timestamp": 1743610661504,

    "error": false,

    "errorDescription": ""

Sparkplug transformation

The previous information will be translated into the sparkplug standard and republish it in the broker. 

The topic will be translated to the following Sparkplug mqtt topic:

spBv1.0/Barbara:Madrid_01/DDATA/kitchen:line1/DoughMixer_MX-5000

Fast & Flexible Deployment with Barbara

Barbara enables easy deployment of your UNS infrastructure through its platform. With pre-integrated open-source applications and a click-to-deploy model, you can go from zero to running in minutes.

Here’s how each level is set up:

UNS: Edge Nodes View in Barbara Panel

Edge Node at Production Line 1 

This edge node operates by collecting machine data, which is then transmitted to the Site Edge Node; concurrently, the data is stored locally, enabling on-site data retention; to facilitate immediate analysis and monitoring, a Grafana dashboard provides local data visualization.

Applications deployed in Edge Node at production line 1

Services:

  • OPC UA/Modbus Connector: Reads data from the machines located at Line 1 and publish data to the local MQTT Sparkplug Broker. 
  • MQTT Sparkplug Broker: Ensures consistent message formatting.
  • MQTT InfluxDB Ingester: Reads data from MQTT broker and stores it in the local InfluxDB database. 
  • InfluxDB: Temporary local storage.
  • Grafana: Real-time dashboards at the line level.
Grafana Dashboard monitorizing realtime data from production line 1

Edge Node at Production Line 2 

Applications deployed in Edge Node at production line 2

This edge node  functions by initially gathering machine data, which is then stored locally for immediate access; subsequently, this collected data is also transmitted to the Site Edge Node for further processing and broader network integration.

Services:

  • OPC UA/Modbus Connector: Reads data from the machines located at Line 1 and publish data to the local MQTT Sparkplug Broker. 
  • MQTT Sparkplug Broker: Acts as the communication backbone, also transforming the information received from the industrial machines to the Sparkplug format.
  • MQTT InfluxDB Ingester: Reads data from MQTT broker and stores it in the local InfluxDB database. 
  • InfluxDB: Temporary local storage.
Data from production line 2 stored in the InfluxDB database

Edge Node at Site Level

This system aggregates data from all line-level nodes, enabling comprehensive cross-line analytics, and even may support advanced functionalities by hosting AI models designed for predictive tasks.

Applications deployed in the Edge Node at the site level

Services:

  • MQTT Sparkplug Broker: Acts as the communication backbone, also transforming the information received from the industrial machines to the Sparkplug format.

Edge Node at Enterprise Level

This upper node level in a Unified Namespace (UNS) architecture facilitates centralized data aggregation and processing, acting as a crucial bridge between operational technology (OT) and information technology (IT) systems. It achieves this connectivity by utilizing MQTT to seamlessly integrate with SCADA systems, ERP platforms, or cloud-based Business Intelligence (BI) tools.

For in-depth analytics, the node incorporates InfluxDB for time-series data management and Ignition SCADA for comprehensive visualization and control, providing a robust platform for data-driven decision-making

Applications deployed in Edge Node at the enterprise level

Services:

  • MQTT Sparkplug Broker: Acts as the communication backbone, also transforming the information received from the industrial machines to the Sparkplug format.
  • MQTT InfluxDB Ingester: Reads data from MQTT broker and stores it in the local database. 
  • InfluxDB: Local time series database.
  • Ignition: Local SCADA.

Production Line 1 realtime information shown in Ignition

Production Line 2 realtime information shown in Ignition

Conclusion

The deployment of a Unified Namespace in a Food & Beverage environment using Barbara’s platform showcases how easy and powerful modern edge solutions can be. With real-time data streaming, local visualization, and seamless AI integration, factories gain full control and insight over their operations ,from individual mixers to enterprise dashboards.

Whether you're starting with basic data collection or looking to add AI-driven maintenance predictions, Barbara provides the tools to get you there faster.

Interested in leveraging your Edge Infrastructure with Barbara? Book a demo!