Software Development at Réalta Technologies: Building Scalable, Secure and User-Focused Digital Solutions

Blog / By

Software Development at Réalta Technologies: Building Scalable, Secure and User-Focused Digital Solutions

As 2025 comes to a close, the data analytics landscape continues to evolve at a pace few industries can match. What was once centred on historical reporting and isolated datasets has matured into a connected, intelligent ecosystem that influences decision-making in real time. For organisations across life sciences, pharmaceuticals, manufacturing, energy and utilities, data is no longer a by-product of operations. It is a strategic asset.

Looking ahead to 2026 and beyond, several clear trends are emerging that will shape how organisations collect, manage, analyse and act on data. These developments are not about adopting the latest technology for its own sake. They are about building resilience, maintaining compliance, improving efficiency and enabling smarter decisions across increasingly complex operations.

 

From Data Collection to Data Intelligence

One of the most significant shifts underway is the move from basic data collection towards true data intelligence. Many organisations have already invested heavily in historians, automation systems and reporting platforms. The challenge now is not access to data, but the ability to contextualise it, trust it and extract meaningful insight from it.

By 2026, successful organisations will be those that have moved beyond disconnected data sources and created well-structured, governed data foundations. This includes consistent naming standards, clear ownership, strong data integrity practices and alignment with operational models such as ISA-95. Without this groundwork, advanced analytics and AI initiatives struggle to deliver value.

 

Artificial Intelligence Becomes Operational, Not Experimental

Artificial Intelligence has dominated recent industry conversations, but its role is now shifting from experimentation to practical, operational use. In regulated industries especially, AI adoption has been cautious, and rightly so. However, we are now seeing a clear move towards AI solutions that are explainable, auditable and aligned with regulatory expectations.

In the years ahead, AI will increasingly be embedded into everyday operational workflows. This includes predictive maintenance, anomaly detection, quality monitoring, demand forecasting and decision support. Rather than replacing human expertise, AI will augment it, enabling engineers, operators and analysts to focus on higher-value tasks while routine analysis runs continuously in the background.

Importantly, organisations will place greater emphasis on trustworthy AI. This means models built on high-quality data, transparent logic and robust validation, particularly in life sciences and pharmaceutical manufacturing where patient safety and compliance are paramount.

 

Real-Time Insight Becomes the Standard

The expectation of real-time or near-real-time insight is becoming the norm rather than the exception. Operational teams increasingly expect to understand what is happening now, not what happened last week. Advances in data infrastructure, streaming technologies and modern visualisation platforms are making this possible at scale.

By 2026, real-time dashboards, alerts and analytics will be embedded across operations, from shop floor monitoring to executive decision-making. This shift supports faster response times, improved operational agility and reduced downtime. It also places greater responsibility on organisations to ensure that real-time data is accurate, contextualised and governed correctly.

 

Greater Focus on Data Architecture and Interoperability

As technology ecosystems become more complex, the importance of strong data architecture continues to grow. Organisations are increasingly recognising that long-term success depends on systems that can evolve without repeated large-scale rework.

Future-ready data strategies will prioritise interoperability between systems, vendors and platforms. This includes automation systems, data historians, analytics tools and enterprise applications working together seamlessly. Open standards, scalable architectures and flexible integration approaches will be key enablers of this trend.

 

Analytics Moves Closer to the Business

Another notable trend is the continued democratisation of data analytics. While deep technical expertise remains essential behind the scenes, analytics tools are becoming more accessible to a wider range of users. Engineers, quality teams and operations managers increasingly expect self-service access to insights without needing to rely on specialist teams for every request.

This does not reduce the need for expert data professionals. On the contrary, it increases the importance of well-designed solutions that balance usability with governance, ensuring that insights are reliable, secure and compliant.

 

Compliance and Data Integrity Remain Non-Negotiable

In regulated industries, compliance and data integrity will continue to underpin every data initiative. As analytics and AI capabilities expand, regulators will expect the same level of control, traceability and validation as traditional systems.

Looking ahead, organisations that successfully integrate compliance into their digital strategies from the outset will be best positioned to innovate with confidence. This includes validation-aware system design, strong change management processes and continuous monitoring of data quality.

 

Preparing for the Future

The future of data analytics is not defined by a single technology or trend. It is shaped by how organisations bring together people, processes and platforms to create sustainable, value-driven solutions. The most successful organisations will be those that invest in strong foundations, adopt emerging technologies pragmatically and partner with experts who understand both the technical and regulatory landscapes.

As we move into 2026 and beyond, data analytics will continue to play a central role in operational excellence, innovation and competitive advantage. The opportunity is significant, but so is the responsibility to implement these capabilities thoughtfully and effectively.

 

To learn more about how Réalta Technologies can help you excel in 2026, contact us on;

 

[email protected]
https://realtatechnologies.com
IRL: +353 21 243 9113 | US: +1 302 509 4401

Software Development at Réalta Technologies: Building Scalable, Secure and User-Focused Digital Solutions

Software Development at Réalta Technologies: Building Scalable, Secure and User-Focused Digital Solutions

As 2025 comes to a close, the data analytics landscape continues to evolve at a pace few industries can match. What was once centred on historical reporting and isolated datasets has matured into a connected, intelligent ecosystem that influences decision-making in real time. For organisations across life sciences, pharmaceuticals, manufacturing, energy and utilities, data is no longer a by-product of operations. It is a strategic asset.

Looking ahead to 2026 and beyond, several clear trends are emerging that will shape how organisations collect, manage, analyse and act on data. These developments are not about adopting the latest technology for its own sake. They are about building resilience, maintaining compliance, improving efficiency and enabling smarter decisions across increasingly complex operations.

 

From Data Collection to Data Intelligence

One of the most significant shifts underway is the move from basic data collection towards true data intelligence. Many organisations have already invested heavily in historians, automation systems and reporting platforms. The challenge now is not access to data, but the ability to contextualise it, trust it and extract meaningful insight from it.

By 2026, successful organisations will be those that have moved beyond disconnected data sources and created well-structured, governed data foundations. This includes consistent naming standards, clear ownership, strong data integrity practices and alignment with operational models such as ISA-95. Without this groundwork, advanced analytics and AI initiatives struggle to deliver value.

 

Artificial Intelligence Becomes Operational, Not Experimental

Artificial Intelligence has dominated recent industry conversations, but its role is now shifting from experimentation to practical, operational use. In regulated industries especially, AI adoption has been cautious, and rightly so. However, we are now seeing a clear move towards AI solutions that are explainable, auditable and aligned with regulatory expectations.

In the years ahead, AI will increasingly be embedded into everyday operational workflows. This includes predictive maintenance, anomaly detection, quality monitoring, demand forecasting and decision support. Rather than replacing human expertise, AI will augment it, enabling engineers, operators and analysts to focus on higher-value tasks while routine analysis runs continuously in the background.

Importantly, organisations will place greater emphasis on trustworthy AI. This means models built on high-quality data, transparent logic and robust validation, particularly in life sciences and pharmaceutical manufacturing where patient safety and compliance are paramount.

 

Real-Time Insight Becomes the Standard

The expectation of real-time or near-real-time insight is becoming the norm rather than the exception. Operational teams increasingly expect to understand what is happening now, not what happened last week. Advances in data infrastructure, streaming technologies and modern visualisation platforms are making this possible at scale.

By 2026, real-time dashboards, alerts and analytics will be embedded across operations, from shop floor monitoring to executive decision-making. This shift supports faster response times, improved operational agility and reduced downtime. It also places greater responsibility on organisations to ensure that real-time data is accurate, contextualised and governed correctly.

 

Greater Focus on Data Architecture and Interoperability

As technology ecosystems become more complex, the importance of strong data architecture continues to grow. Organisations are increasingly recognising that long-term success depends on systems that can evolve without repeated large-scale rework.

Future-ready data strategies will prioritise interoperability between systems, vendors and platforms. This includes automation systems, data historians, analytics tools and enterprise applications working together seamlessly. Open standards, scalable architectures and flexible integration approaches will be key enablers of this trend.

 

Analytics Moves Closer to the Business

Another notable trend is the continued democratisation of data analytics. While deep technical expertise remains essential behind the scenes, analytics tools are becoming more accessible to a wider range of users. Engineers, quality teams and operations managers increasingly expect self-service access to insights without needing to rely on specialist teams for every request.

This does not reduce the need for expert data professionals. On the contrary, it increases the importance of well-designed solutions that balance usability with governance, ensuring that insights are reliable, secure and compliant.

 

Compliance and Data Integrity Remain Non-Negotiable

In regulated industries, compliance and data integrity will continue to underpin every data initiative. As analytics and AI capabilities expand, regulators will expect the same level of control, traceability and validation as traditional systems.

Looking ahead, organisations that successfully integrate compliance into their digital strategies from the outset will be best positioned to innovate with confidence. This includes validation-aware system design, strong change management processes and continuous monitoring of data quality.

 

Preparing for the Future

The future of data analytics is not defined by a single technology or trend. It is shaped by how organisations bring together people, processes and platforms to create sustainable, value-driven solutions. The most successful organisations will be those that invest in strong foundations, adopt emerging technologies pragmatically and partner with experts who understand both the technical and regulatory landscapes.

As we move into 2026 and beyond, data analytics will continue to play a central role in operational excellence, innovation and competitive advantage. The opportunity is significant, but so is the responsibility to implement these capabilities thoughtfully and effectively.

 

To learn more about how Réalta Technologies can help you excel in 2026, contact us on;

 

[email protected]
https://realtatechnologies.com
IRL: +353 21 243 9113 | US: +1 302 509 4401

Software Development at Réalta Technologies: Building Scalable, Secure and User-Focused Digital Solutions

Software Development at Réalta Technologies: Building Scalable, Secure and User-Focused Digital Solutions Read More »

The Future of Data Analytics and Industry Trends for 2026 and Beyond

Blog / By

The Future of Data Analytics and Industry Trends for 2026 and Beyond

As 2025 comes to a close, the data analytics landscape continues to evolve at a pace few industries can match. What was once centred on historical reporting and isolated datasets has matured into a connected, intelligent ecosystem that influences decision-making in real time. For organisations across life sciences, pharmaceuticals, manufacturing, energy and utilities, data is no longer a by-product of operations. It is a strategic asset.

Looking ahead to 2026 and beyond, several clear trends are emerging that will shape how organisations collect, manage, analyse and act on data. These developments are not about adopting the latest technology for its own sake. They are about building resilience, maintaining compliance, improving efficiency and enabling smarter decisions across increasingly complex operations.

 

From Data Collection to Data Intelligence

One of the most significant shifts underway is the move from basic data collection towards true data intelligence. Many organisations have already invested heavily in historians, automation systems and reporting platforms. The challenge now is not access to data, but the ability to contextualise it, trust it and extract meaningful insight from it.

By 2026, successful organisations will be those that have moved beyond disconnected data sources and created well-structured, governed data foundations. This includes consistent naming standards, clear ownership, strong data integrity practices and alignment with operational models such as ISA-95. Without this groundwork, advanced analytics and AI initiatives struggle to deliver value.

 

Artificial Intelligence Becomes Operational, Not Experimental

Artificial Intelligence has dominated recent industry conversations, but its role is now shifting from experimentation to practical, operational use. In regulated industries especially, AI adoption has been cautious, and rightly so. However, we are now seeing a clear move towards AI solutions that are explainable, auditable and aligned with regulatory expectations.

In the years ahead, AI will increasingly be embedded into everyday operational workflows. This includes predictive maintenance, anomaly detection, quality monitoring, demand forecasting and decision support. Rather than replacing human expertise, AI will augment it, enabling engineers, operators and analysts to focus on higher-value tasks while routine analysis runs continuously in the background.

Importantly, organisations will place greater emphasis on trustworthy AI. This means models built on high-quality data, transparent logic and robust validation, particularly in life sciences and pharmaceutical manufacturing where patient safety and compliance are paramount.

 

Real-Time Insight Becomes the Standard

The expectation of real-time or near-real-time insight is becoming the norm rather than the exception. Operational teams increasingly expect to understand what is happening now, not what happened last week. Advances in data infrastructure, streaming technologies and modern visualisation platforms are making this possible at scale.

By 2026, real-time dashboards, alerts and analytics will be embedded across operations, from shop floor monitoring to executive decision-making. This shift supports faster response times, improved operational agility and reduced downtime. It also places greater responsibility on organisations to ensure that real-time data is accurate, contextualised and governed correctly.

 

Greater Focus on Data Architecture and Interoperability

As technology ecosystems become more complex, the importance of strong data architecture continues to grow. Organisations are increasingly recognising that long-term success depends on systems that can evolve without repeated large-scale rework.

Future-ready data strategies will prioritise interoperability between systems, vendors and platforms. This includes automation systems, data historians, analytics tools and enterprise applications working together seamlessly. Open standards, scalable architectures and flexible integration approaches will be key enablers of this trend.

 

Analytics Moves Closer to the Business

Another notable trend is the continued democratisation of data analytics. While deep technical expertise remains essential behind the scenes, analytics tools are becoming more accessible to a wider range of users. Engineers, quality teams and operations managers increasingly expect self-service access to insights without needing to rely on specialist teams for every request.

This does not reduce the need for expert data professionals. On the contrary, it increases the importance of well-designed solutions that balance usability with governance, ensuring that insights are reliable, secure and compliant.

 

Compliance and Data Integrity Remain Non-Negotiable

In regulated industries, compliance and data integrity will continue to underpin every data initiative. As analytics and AI capabilities expand, regulators will expect the same level of control, traceability and validation as traditional systems.

Looking ahead, organisations that successfully integrate compliance into their digital strategies from the outset will be best positioned to innovate with confidence. This includes validation-aware system design, strong change management processes and continuous monitoring of data quality.

 

Preparing for the Future

The future of data analytics is not defined by a single technology or trend. It is shaped by how organisations bring together people, processes and platforms to create sustainable, value-driven solutions. The most successful organisations will be those that invest in strong foundations, adopt emerging technologies pragmatically and partner with experts who understand both the technical and regulatory landscapes.

As we move into 2026 and beyond, data analytics will continue to play a central role in operational excellence, innovation and competitive advantage. The opportunity is significant, but so is the responsibility to implement these capabilities thoughtfully and effectively.

 

To learn more about how Réalta Technologies can help you excel in 2026, contact us on;

 

[email protected]
https://realtatechnologies.com
IRL: +353 21 243 9113 | US: +1 302 509 4401

The Future of Data Analytics and Industry Trends for 2026 and Beyond

The Future of Data Analytics and Industry Trends for 2026 and Beyond

As 2025 comes to a close, the data analytics landscape continues to evolve at a pace few industries can match. What was once centred on historical reporting and isolated datasets has matured into a connected, intelligent ecosystem that influences decision-making in real time. For organisations across life sciences, pharmaceuticals, manufacturing, energy and utilities, data is no longer a by-product of operations. It is a strategic asset.

Looking ahead to 2026 and beyond, several clear trends are emerging that will shape how organisations collect, manage, analyse and act on data. These developments are not about adopting the latest technology for its own sake. They are about building resilience, maintaining compliance, improving efficiency and enabling smarter decisions across increasingly complex operations.

 

From Data Collection to Data Intelligence

One of the most significant shifts underway is the move from basic data collection towards true data intelligence. Many organisations have already invested heavily in historians, automation systems and reporting platforms. The challenge now is not access to data, but the ability to contextualise it, trust it and extract meaningful insight from it.

By 2026, successful organisations will be those that have moved beyond disconnected data sources and created well-structured, governed data foundations. This includes consistent naming standards, clear ownership, strong data integrity practices and alignment with operational models such as ISA-95. Without this groundwork, advanced analytics and AI initiatives struggle to deliver value.

 

Artificial Intelligence Becomes Operational, Not Experimental

Artificial Intelligence has dominated recent industry conversations, but its role is now shifting from experimentation to practical, operational use. In regulated industries especially, AI adoption has been cautious, and rightly so. However, we are now seeing a clear move towards AI solutions that are explainable, auditable and aligned with regulatory expectations.

In the years ahead, AI will increasingly be embedded into everyday operational workflows. This includes predictive maintenance, anomaly detection, quality monitoring, demand forecasting and decision support. Rather than replacing human expertise, AI will augment it, enabling engineers, operators and analysts to focus on higher-value tasks while routine analysis runs continuously in the background.

Importantly, organisations will place greater emphasis on trustworthy AI. This means models built on high-quality data, transparent logic and robust validation, particularly in life sciences and pharmaceutical manufacturing where patient safety and compliance are paramount.

 

Real-Time Insight Becomes the Standard

The expectation of real-time or near-real-time insight is becoming the norm rather than the exception. Operational teams increasingly expect to understand what is happening now, not what happened last week. Advances in data infrastructure, streaming technologies and modern visualisation platforms are making this possible at scale.

By 2026, real-time dashboards, alerts and analytics will be embedded across operations, from shop floor monitoring to executive decision-making. This shift supports faster response times, improved operational agility and reduced downtime. It also places greater responsibility on organisations to ensure that real-time data is accurate, contextualised and governed correctly.

 

Greater Focus on Data Architecture and Interoperability

As technology ecosystems become more complex, the importance of strong data architecture continues to grow. Organisations are increasingly recognising that long-term success depends on systems that can evolve without repeated large-scale rework.

Future-ready data strategies will prioritise interoperability between systems, vendors and platforms. This includes automation systems, data historians, analytics tools and enterprise applications working together seamlessly. Open standards, scalable architectures and flexible integration approaches will be key enablers of this trend.

 

Analytics Moves Closer to the Business

Another notable trend is the continued democratisation of data analytics. While deep technical expertise remains essential behind the scenes, analytics tools are becoming more accessible to a wider range of users. Engineers, quality teams and operations managers increasingly expect self-service access to insights without needing to rely on specialist teams for every request.

This does not reduce the need for expert data professionals. On the contrary, it increases the importance of well-designed solutions that balance usability with governance, ensuring that insights are reliable, secure and compliant.

 

Compliance and Data Integrity Remain Non-Negotiable

In regulated industries, compliance and data integrity will continue to underpin every data initiative. As analytics and AI capabilities expand, regulators will expect the same level of control, traceability and validation as traditional systems.

Looking ahead, organisations that successfully integrate compliance into their digital strategies from the outset will be best positioned to innovate with confidence. This includes validation-aware system design, strong change management processes and continuous monitoring of data quality.

 

Preparing for the Future

The future of data analytics is not defined by a single technology or trend. It is shaped by how organisations bring together people, processes and platforms to create sustainable, value-driven solutions. The most successful organisations will be those that invest in strong foundations, adopt emerging technologies pragmatically and partner with experts who understand both the technical and regulatory landscapes.

As we move into 2026 and beyond, data analytics will continue to play a central role in operational excellence, innovation and competitive advantage. The opportunity is significant, but so is the responsibility to implement these capabilities thoughtfully and effectively.

 

To learn more about how Réalta Technologies can help you excel in 2026, contact us on;

 

[email protected]
https://realtatechnologies.com
IRL: +353 21 243 9113 | US: +1 302 509 4401

The Future of Data Analytics and Industry Trends for 2026 and Beyond

The Future of Data Analytics and Industry Trends for 2026 and Beyond Read More »

Réalta Technologies Joins TDengine Partner Network as a Value-Added Reseller

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Réalta Technologies Joins TDengine Partner Network as a Value-Added Reseller

TDengine today announced that Réalta Technologies, an Ireland-based global automation and digital systems integrator, has joined the TDengine Reseller Program as a value-added reseller (VAR). The partnership expands TDengine’s worldwide ecosystem and strengthens its ability to deliver high-performance time-series and industrial data solutions to manufacturing and life sciences customers.

 

Through this collaboration, Réalta will offer TDengine’s industry-leading time-series database and AI-native industrial data management platform to enterprises across pharmaceutical, biopharmaceutical, medical device, FMCG, and general manufacturing sectors. As a value-added reseller, Réalta will provide deep expertise in automation, data infrastructure, analytics, and integration, helping clients modernize operations and accelerate Industry 4.0 adoption.

 

“TDengine is revolutionizing how industrial data is collected, stored, and analyzed,” said Jim Fan, VP of Product at TDengine. “Réalta Technologies’ broad experience in digital systems integration and process optimization makes them an exceptional partner to extend TDengine’s reach and help customers achieve faster, more data-driven operations.”

 

Réalta Technologies is a global automation and digital systems integrator headquartered in Cork, Ireland, with offices in Cork, the United States, and India. The company provides automation, digitalization, and data analytics solutions for the life sciences, pharmaceutical, manufacturing, and other industries. With deep expertise across engineering, industrial automation, IT systems, and data infrastructure, Réalta helps clients maximize the value of their data and achieve true Industry 4.0 transformation.

 

“We’re proud to partner with TDengine to bring their innovative time-series data technology to our clients,” said Dan Moore, CEO of Réalta Technologies. “Our mission is to help organizations harness the full power of their data and drive manufacturing excellence from automation to analytics. With TDengine’s platform for operational data storage and management, we can deliver even greater value through performance, scalability, and real-time intelligence.”

 

By combining Réalta’s global presence and industry expertise with TDengine’s high-performance, AI-native industrial data platform, customers will benefit from streamlined deployments, simplified operations, and a lower total cost of ownership across on-premises, cloud, and hybrid environments.

Réalta Technologies Joins TDengine Partner Network as a Value-Added Reseller

Réalta Technologies Joins TDengine Partner Network as a Value-Added Reseller

TDengine today announced that Réalta Technologies, an Ireland-based global automation and digital systems integrator, has joined the TDengine Reseller Program as a value-added reseller (VAR). The partnership expands TDengine’s worldwide ecosystem and strengthens its ability to deliver high-performance time-series and industrial data solutions to manufacturing and life sciences customers.

 

Through this collaboration, Réalta will offer TDengine’s industry-leading time-series database and AI-native industrial data management platform to enterprises across pharmaceutical, biopharmaceutical, medical device, FMCG, and general manufacturing sectors. As a value-added reseller, Réalta will provide deep expertise in automation, data infrastructure, analytics, and integration, helping clients modernize operations and accelerate Industry 4.0 adoption.

 

“TDengine is revolutionizing how industrial data is collected, stored, and analyzed,” said Jim Fan, VP of Product at TDengine. “Réalta Technologies’ broad experience in digital systems integration and process optimization makes them an exceptional partner to extend TDengine’s reach and help customers achieve faster, more data-driven operations.”

 

Réalta Technologies is a global automation and digital systems integrator headquartered in Cork, Ireland, with offices in Cork, the United States, and India. The company provides automation, digitalization, and data analytics solutions for the life sciences, pharmaceutical, manufacturing, and other industries. With deep expertise across engineering, industrial automation, IT systems, and data infrastructure, Réalta helps clients maximize the value of their data and achieve true Industry 4.0 transformation.

 

“We’re proud to partner with TDengine to bring their innovative time-series data technology to our clients,” said Dan Moore, CEO of Réalta Technologies. “Our mission is to help organizations harness the full power of their data and drive manufacturing excellence from automation to analytics. With TDengine’s platform for operational data storage and management, we can deliver even greater value through performance, scalability, and real-time intelligence.”

 

By combining Réalta’s global presence and industry expertise with TDengine’s high-performance, AI-native industrial data platform, customers will benefit from streamlined deployments, simplified operations, and a lower total cost of ownership across on-premises, cloud, and hybrid environments.

Réalta Technologies Joins TDengine Partner Network as a Value-Added Reseller

Réalta Technologies Joins TDengine Partner Network as a Value-Added Reseller Read More »

Data Historian Spotlight: Canary Historian’s Role in Life Sciences

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Data Historian Spotlight: Canary Historian’s Role in Life Sciences

Introduction: 

In the world of pharmaceutical and life sciences manufacturing, the ability to capture, contextualise, and analyse data in real time is essential. Modern operations depend on reliable, validated data systems that not only meet strict compliance standards but also empower continuous improvement, efficiency, and innovation. Among the leading data historian technologies driving this transformation is Canary Historian, a powerful, scalable solution trusted by manufacturers worldwide.

As specialists in data infrastructure and data analytics, Réalta Technologies works closely with clients to implement historian systems like Canary, helping them achieve visibility, reliability, and data integrity across their operations.

 

What Is Canary Historian?

Canary Historian, developed by Canary Labs, is a high-performance, enterprise-grade data historian designed to store, manage, and analyse time-series data. Built for speed, reliability, and scalability, it provides life sciences organisations with a secure and compliant platform for collecting data from sensors, control systems, and industrial devices.

Unlike traditional historians that can be complex to maintain or scale, Canary’s architecture is lightweight and efficient, allowing for fast data ingestion and retrieval without compromising integrity or performance. It seamlessly integrates with control systems like Siemens, Rockwell, Emerson, and Ignition front end screens, as well as analytics and reporting platforms such as Power BI and SEEQ.

 

Key Capabilities of Canary Historian

Canary Historian is built with a clear focus on data integrity, speed, and accessibility, all essential criteria in regulated environments such as pharmaceutical manufacturing.

  • Lossless Data Compression:
    Canary’s patented compression algorithms allow massive volumes of process data to be stored efficiently while preserving accuracy. This ensures traceability and compliance with regulatory frameworks like FDA 21 CFR Part 11 and EU Annex 11.
  • High-Performance Data Retrieval:
    Canary is optimised for fast query performance, enabling engineers, data scientists, and quality teams to access and visualise data instantly, even across years of historical information.
  • Scalability and Flexibility:
    Designed to scale from a single production line to global enterprise deployments, Canary can handle millions of data points per second, supporting digital transformation initiatives across multiple sites.
  • Integration with Analytics Platforms:
    Through seamless integration with SEEQ, Power BI, and other modern analytics tools, Canary allows users to move from raw process data to actionable insights. Additionally, Canary can integrate its UNS architecture seamlessly by collecting data in MQTT & SPBv1.0 specifications from MQTT data sources.
    This empowers smarter decision-making and accelerates continuous improvement. 
  • Security and Compliance:
    Canary supports role-based access control, data encryption, and full audit trails , critical for compliance in GxP environments.

 

Use Cases in Life Sciences and Pharmaceutical Manufacturing

For pharmaceutical and biotech manufacturers, data integrity and process optimisation are non-negotiable. Canary Historian plays a crucial role across a range of applications:

  • Batch Process Monitoring:
    Ensuring each batch follows the defined recipe and identifying deviations or anomalies early in production.
  • Equipment Performance Tracking:
    Continuous monitoring of critical systems such as bioreactors, cleanrooms, and HVAC to ensure operational reliability and product quality.
  • Regulatory Compliance and Audit Readiness:
    Providing detailed data trails that demonstrate compliance with GMP standards and regulatory requirements.
  • Energy and Utility Management:
    Capturing and analysing utility data, from compressed air to chilled water, to optimise energy consumption and sustainability initiatives.
  • Predictive Maintenance and Quality Analytics:
    When combined with advanced analytics platforms, Canary enables manufacturers to predict equipment failures before they occur and improve product consistency through process insights.

 

Data Collection, Visualisation, and Analytics

At its core, Canary is more than a historian, it is a data foundation for innovation. By centralising time-series data and contextualising it within the manufacturing ecosystem, it enables seamless visualisation and analysis.

Through integrations with platforms like SEEQ, engineers can build advanced analytics models, track key performance indicators (KPIs), and uncover correlations between process parameters and product quality. This real-time visibility leads to more efficient operations, reduced downtime, and data-driven decision-making across departments.

 

How Réalta Technologies Adds Value with Canary Historian

As a trusted data partner to global life sciences organisations, Réalta Technologies has extensive experience implementing and optimising Canary Historian systems. Our engineers understand both the technical and regulatory dimensions of data management, ensuring that each deployment is compliant, scalable, and future-ready.

Our expertise spans:

  • Designing and deploying data historian architectures across multi-site facilities.
  • Integrating Canary with control systems and enterprise applications.
  • Enabling connectivity to SEEQ, Power BI, and advanced analytics frameworks.
  • Supporting validation, testing, and documentation to meet regulatory expectations.

Whether you are upgrading from legacy historians or implementing a new data infrastructure, Réalta Technologies provides a complete solution , from design and deployment to ongoing managed services.

 

Conclusion

In today’s data-driven manufacturing landscape, the ability to collect, contextualise, and analyse process data efficiently is a competitive advantage. Canary Historian provides life sciences companies with the flexibility, speed, and compliance they need to turn data into real value.

Partnering with Réalta Technologies ensures that this technology is implemented with precision and aligned with your business goals. With proven expertise in data historians, automation, and analytics, we empower organisations to achieve operational excellence through data.

 

To learn more about how Réalta Technologies can help you implement or optimise Canary Historian, contact our team today.

📧 [email protected]
💻 https://realtatechnologies.com
📞 IRL: +353 21 243 9113 | US: +1 302 509 4401

Data Historian Spotlight: Canary Historian’s Role in Life Sciences

Data Historian Spotlight: Canary Historian’s Role in Life Sciences

Introduction: 

In the world of pharmaceutical and life sciences manufacturing, the ability to capture, contextualise, and analyse data in real time is essential. Modern operations depend on reliable, validated data systems that not only meet strict compliance standards but also empower continuous improvement, efficiency, and innovation. Among the leading data historian technologies driving this transformation is Canary Historian, a powerful, scalable solution trusted by manufacturers worldwide.

As specialists in data infrastructure and data analytics, Réalta Technologies works closely with clients to implement historian systems like Canary, helping them achieve visibility, reliability, and data integrity across their operations.

 

What Is Canary Historian?

Canary Historian, developed by Canary Labs, is a high-performance, enterprise-grade data historian designed to store, manage, and analyse time-series data. Built for speed, reliability, and scalability, it provides life sciences organisations with a secure and compliant platform for collecting data from sensors, control systems, and industrial devices.

Unlike traditional historians that can be complex to maintain or scale, Canary’s architecture is lightweight and efficient, allowing for fast data ingestion and retrieval without compromising integrity or performance. It seamlessly integrates with control systems like Siemens, Rockwell, Emerson, and Ignition front end screens, as well as analytics and reporting platforms such as Power BI and SEEQ.

 

Key Capabilities of Canary Historian

Canary Historian is built with a clear focus on data integrity, speed, and accessibility, all essential criteria in regulated environments such as pharmaceutical manufacturing.

  • Lossless Data Compression:
    Canary’s patented compression algorithms allow massive volumes of process data to be stored efficiently while preserving accuracy. This ensures traceability and compliance with regulatory frameworks like FDA 21 CFR Part 11 and EU Annex 11.
  • High-Performance Data Retrieval:
    Canary is optimised for fast query performance, enabling engineers, data scientists, and quality teams to access and visualise data instantly, even across years of historical information.
  • Scalability and Flexibility:
    Designed to scale from a single production line to global enterprise deployments, Canary can handle millions of data points per second, supporting digital transformation initiatives across multiple sites.
  • Integration with Analytics Platforms:
    Through seamless integration with SEEQ, Power BI, and other modern analytics tools, Canary allows users to move from raw process data to actionable insights. Additionally, Canary can integrate its UNS architecture seamlessly by collecting data in MQTT & SPBv1.0 specifications from MQTT data sources.
    This empowers smarter decision-making and accelerates continuous improvement. 
  • Security and Compliance:
    Canary supports role-based access control, data encryption, and full audit trails , critical for compliance in GxP environments.

 

Use Cases in Life Sciences and Pharmaceutical Manufacturing

For pharmaceutical and biotech manufacturers, data integrity and process optimisation are non-negotiable. Canary Historian plays a crucial role across a range of applications:

  • Batch Process Monitoring:
    Ensuring each batch follows the defined recipe and identifying deviations or anomalies early in production.
  • Equipment Performance Tracking:
    Continuous monitoring of critical systems such as bioreactors, cleanrooms, and HVAC to ensure operational reliability and product quality.
  • Regulatory Compliance and Audit Readiness:
    Providing detailed data trails that demonstrate compliance with GMP standards and regulatory requirements.
  • Energy and Utility Management:
    Capturing and analysing utility data, from compressed air to chilled water, to optimise energy consumption and sustainability initiatives.
  • Predictive Maintenance and Quality Analytics:
    When combined with advanced analytics platforms, Canary enables manufacturers to predict equipment failures before they occur and improve product consistency through process insights.

 

Data Collection, Visualisation, and Analytics

At its core, Canary is more than a historian, it is a data foundation for innovation. By centralising time-series data and contextualising it within the manufacturing ecosystem, it enables seamless visualisation and analysis.

Through integrations with platforms like SEEQ, engineers can build advanced analytics models, track key performance indicators (KPIs), and uncover correlations between process parameters and product quality. This real-time visibility leads to more efficient operations, reduced downtime, and data-driven decision-making across departments.

 

How Réalta Technologies Adds Value with Canary Historian

As a trusted data partner to global life sciences organisations, Réalta Technologies has extensive experience implementing and optimising Canary Historian systems. Our engineers understand both the technical and regulatory dimensions of data management, ensuring that each deployment is compliant, scalable, and future-ready.

Our expertise spans:

  • Designing and deploying data historian architectures across multi-site facilities.
  • Integrating Canary with control systems and enterprise applications.
  • Enabling connectivity to SEEQ, Power BI, and advanced analytics frameworks.
  • Supporting validation, testing, and documentation to meet regulatory expectations.

Whether you are upgrading from legacy historians or implementing a new data infrastructure, Réalta Technologies provides a complete solution , from design and deployment to ongoing managed services.

 

Conclusion

In today’s data-driven manufacturing landscape, the ability to collect, contextualise, and analyse process data efficiently is a competitive advantage. Canary Historian provides life sciences companies with the flexibility, speed, and compliance they need to turn data into real value.

Partnering with Réalta Technologies ensures that this technology is implemented with precision and aligned with your business goals. With proven expertise in data historians, automation, and analytics, we empower organisations to achieve operational excellence through data.

 

To learn more about how Réalta Technologies can help you implement or optimise Canary Historian, contact our team today.

📧 [email protected]
💻 https://realtatechnologies.com
📞 IRL: +353 21 243 9113 | US: +1 302 509 4401

Data Historian Spotlight: Canary Historian’s Role in Life Sciences

Data Historian Spotlight: Canary Historian’s Role in Life Sciences Read More »

Turning Data into Action: Réalta Technologies Hosts Industry and Sport Event at Thomond Park

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Turning Data into Action: Réalta Technologies Hosts Industry and Sport Event at Thomond Park

Introduction: 

Réalta Technologies recently hosted an exclusive event at Thomond Park, home of Munster Rugby, exploring how data is driving performance and decision-making across both industry and sport. The event, Turning Data into Action: Unlocking Real-Time Insights for Operational Excellence in Industry and Sport, brought together clients, partners, and industry leaders for an engaging day of learning, collaboration, and discussion.

Guests enjoyed a behind-the-scenes tour of Thomond Park and a series of presentations and panel discussions featuring thought leaders from Réalta Technologies, AVEVA, SolutionsPT, and Munster Rugby.

 

Shaping the Future with Digital Twins – Andy Davidson, AVEVA

Andy Davidson, Product Manager at AVEVA, opened the day with an insightful session on Building and Evolving a Digital Twin. He explained how digital twins bring together connected data and intelligent insight to improve decision-making, efficiency, and performance. Andy highlighted how these technologies are already transforming industries and how AVEVA’s scalable digital solutions empower businesses of all sizes to operate more intelligently.

 

Digitalisation of the 3D Printing Process – Declan Hickey, Réalta Technologies

Declan Hickey, Principal Engineer at Réalta Technologies, delivered an engaging session on Digitalisation of the 3D Printing Process. Declan explored how automation, data integration, and digital workflows can transform additive manufacturing, improving traceability, quality, and efficiency at every stage. 

He outlined how connecting equipment, materials, and production data within a unified digital framework enables manufacturers to achieve greater consistency, scalability, and regulatory compliance. Drawing on Réalta’s extensive experience in life sciences and advanced manufacturing, Declan demonstrated how a data-driven approach can unlock the full potential of 3D printing in regulated industries.

 

AI in Life Sciences – Thomas McCarthy, AVEVA

Next, Thomas McCarthy, Industry Principal at AVEVA, delivered a compelling talk on Artificial Intelligence in Life Sciences. Thomas outlined how AI and data integration are revolutionising the pharmaceutical sector, from pre-clinical research and development through to manufacturing and patient outcomes. His talk showcased how AI-driven data ecosystems can accelerate innovation, enhance quality, and optimise operations across the life sciences value chain.

 

From Equipment to Enterprise: Achieving Data Integration at a Global Scale – Réalta Technologies & Pharma Client

Nikhil Ramisetty and Andreas Scannell from Réalta Technologies were joined by a Value Stream Leader from a leading pharmaceutical client to deliver an insightful joint presentation on their collaborative work. Titled “From Equipment to Enterprise: Achieving Data Integration at a Global Scale:, the session detailed their shared journey in implementing a full-scale, GxP-compliant AVEVA PI System and advanced data analytics solution across the client sites. 

Together, they outlined how a unified data infrastructure can transform visibility, efficiency, and decision-making across complex operations. The speakers discussed the project’s key challenges, the importance of collaboration, and how the integration of data from equipment to enterprise level enables greater standardisation, compliance, and operational excellence in regulated environments.

 

Generative AI and Large Language Models – Ken Molloy, SolutionsPT

Ken Molloy, Customer Success Manager at SolutionsPT, delivered an engaging session on Generative AI and Large Language Models, examining the rapid advancements reshaping today’s industrial landscape. Ken provided an in-depth look at how generative AI is already driving innovation, efficiency, and smarter automation across industries. He also highlighted how SolutionsPT supports organisations through comprehensive education, training, audits, consulting, and customer success management, empowering teams to adapt, evolve, and succeed with confidence.

 

Data in Sport – George Murray & Munster Rugby

George Murray, Lead Performance Analyst for Munster Rugby, shared fascinating insights into how data analysis supports high performance within elite sport. He was joined by Damien Falvey of Réalta Technologies to discuss Munster’s data-driven approach and how Réalta Technologies are helping coaches and players optimise performance, manage workloads, and gain competitive advantage, illustrating clear parallels between how data delivers value both on the pitch and in industry.

The event concluded with a panel discussion hosted by Barry Murphy, former Munster Rugby player, who was joined by Munster Rugby coaches and players to gain an insight into how they use data to improve their performance and find that extra 1% in competitive edge. 

 

A Collaborative Success

Réalta Technologies would like to extend sincere thanks to AVEVA and SolutionsPT for their sponsorship and support in making the day possible, to Munster Rugby for their hospitality and partnership, and to all who attended and contributed to such a successful event.

Finally, a special thanks to the Réalta Technologies team whose effort and expertise made the event a resounding success. A special thanks to Damien Falvey, Nikhil Ramisetty, Andreas Scannell, and Declan Hickey for representing Réalta on stage and sharing their expertise with the audience.

Réalta looks forward to hosting more events that bring together leaders across industry and sport to explore how data can unlock the next era of innovation and performance.

Turning Data into Action: Réalta Technologies Hosts Industry and Sport Event at Thomond Park

Turning Data into Action: Réalta Technologies Hosts Industry and Sport Event at Thomond Park

Introduction: 

Réalta Technologies recently hosted an exclusive event at Thomond Park, home of Munster Rugby, exploring how data is driving performance and decision-making across both industry and sport. The event, Turning Data into Action: Unlocking Real-Time Insights for Operational Excellence in Industry and Sport, brought together clients, partners, and industry leaders for an engaging day of learning, collaboration, and discussion.

Guests enjoyed a behind-the-scenes tour of Thomond Park and a series of presentations and panel discussions featuring thought leaders from Réalta Technologies, AVEVA, SolutionsPT, and Munster Rugby.

 

Shaping the Future with Digital Twins – Andy Davidson, AVEVA

Andy Davidson, Product Manager at AVEVA, opened the day with an insightful session on Building and Evolving a Digital Twin. He explained how digital twins bring together connected data and intelligent insight to improve decision-making, efficiency, and performance. Andy highlighted how these technologies are already transforming industries and how AVEVA’s scalable digital solutions empower businesses of all sizes to operate more intelligently.

 

Digitalisation of the 3D Printing Process – Declan Hickey, Réalta Technologies

Declan Hickey, Principal Engineer at Réalta Technologies, delivered an engaging session on Digitalisation of the 3D Printing Process. Declan explored how automation, data integration, and digital workflows can transform additive manufacturing, improving traceability, quality, and efficiency at every stage. 

He outlined how connecting equipment, materials, and production data within a unified digital framework enables manufacturers to achieve greater consistency, scalability, and regulatory compliance. Drawing on Réalta’s extensive experience in life sciences and advanced manufacturing, Declan demonstrated how a data-driven approach can unlock the full potential of 3D printing in regulated industries.

 

AI in Life Sciences – Thomas McCarthy, AVEVA

Next, Thomas McCarthy, Industry Principal at AVEVA, delivered a compelling talk on Artificial Intelligence in Life Sciences. Thomas outlined how AI and data integration are revolutionising the pharmaceutical sector, from pre-clinical research and development through to manufacturing and patient outcomes. His talk showcased how AI-driven data ecosystems can accelerate innovation, enhance quality, and optimise operations across the life sciences value chain.

 

From Equipment to Enterprise: Achieving Data Integration at a Global Scale – Réalta Technologies & Pharma Client

Nikhil Ramisetty and Andreas Scannell from Réalta Technologies were joined by a Value Stream Leader from a leading pharmaceutical client to deliver an insightful joint presentation on their collaborative work. Titled “From Equipment to Enterprise: Achieving Data Integration at a Global Scale:, the session detailed their shared journey in implementing a full-scale, GxP-compliant AVEVA PI System and advanced data analytics solution across the client sites. 

Together, they outlined how a unified data infrastructure can transform visibility, efficiency, and decision-making across complex operations. The speakers discussed the project’s key challenges, the importance of collaboration, and how the integration of data from equipment to enterprise level enables greater standardisation, compliance, and operational excellence in regulated environments.

 

Generative AI and Large Language Models – Ken Molloy, SolutionsPT

Ken Molloy, Customer Success Manager at SolutionsPT, delivered an engaging session on Generative AI and Large Language Models, examining the rapid advancements reshaping today’s industrial landscape. Ken provided an in-depth look at how generative AI is already driving innovation, efficiency, and smarter automation across industries. He also highlighted how SolutionsPT supports organisations through comprehensive education, training, audits, consulting, and customer success management, empowering teams to adapt, evolve, and succeed with confidence.

 

Data in Sport – George Murray & Munster Rugby

George Murray, Lead Performance Analyst for Munster Rugby, shared fascinating insights into how data analysis supports high performance within elite sport. He was joined by Damien Falvey of Réalta Technologies to discuss Munster’s data-driven approach and how Réalta Technologies are helping coaches and players optimise performance, manage workloads, and gain competitive advantage, illustrating clear parallels between how data delivers value both on the pitch and in industry.

The event concluded with a panel discussion hosted by Barry Murphy, former Munster Rugby player, who was joined by Munster Rugby coaches and players to gain an insight into how they use data to improve their performance and find that extra 1% in competitive edge. 

 

A Collaborative Success

Réalta Technologies would like to extend sincere thanks to AVEVA and SolutionsPT for their sponsorship and support in making the day possible, to Munster Rugby for their hospitality and partnership, and to all who attended and contributed to such a successful event.

Finally, a special thanks to the Réalta Technologies team whose effort and expertise made the event a resounding success. A special thanks to Damien Falvey, Nikhil Ramisetty, Andreas Scannell, and Declan Hickey for representing Réalta on stage and sharing their expertise with the audience.

Réalta looks forward to hosting more events that bring together leaders across industry and sport to explore how data can unlock the next era of innovation and performance.

Turning Data into Action: Réalta Technologies Hosts Industry and Sport Event at Thomond Park

Turning Data into Action: Réalta Technologies Hosts Industry and Sport Event at Thomond Park Read More »

Realta Technologies Pi System Upgrade

PI System Upgrade Case Study for Pharmaceutical Manufacturing | Réalta Technologies

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PI System Upgrade Case Study for Pharmaceutical Manufacturing | Réalta Technologies

Introduction: 

Réalta Technologies recently completed a critical PI System upgrade for a leading pharmaceutical manufacturer. The project involved replacing ageing hardware and unsupported operating systems, ensuring the client’s data infrastructure met modern performance, security, and compliance standards. 

 

As a trusted partner and AVEVA Endorsed System Integrator, Réalta Technologies delivered a seamless transition to a fully upgraded system without data loss, enabling the client to continue operations without disruption and minimal downtime. 

 

The Challenge

The client’s PI System servers were operating on outdated hardware and operating systems. An upgrade-in-place was not possible due to:

  • Incompatibility with newer OS versions and software.
  • Requirement for new hardware to meet performance demands.
  • The need to keep existing servers intact for rollback in case of an issue.
  • The critical need to avoid any data loss during the transition.

These constraints required a carefully designed migration strategy that balanced operational continuity, data integrity, and validation requirements.

 

Possible Solutions

Drawing on extensive experience in industrial data infrastructure projects, Réalta Technologies identified three potential upgrade strategies:

 

Option 1: Adding New Servers to the PI Collective

  • Build new servers on new hardware and OS.
  • Add them to the existing PI System Collective.
  • Promote one of the new servers to Primary Data Archive.
  • Remove old servers once complete.

Pros: Minimal downtime, data available to users throughout.
Cons: Requires reconfiguring and restarting all interfaces, which can be time-consuming with large systems.

 

Option 2: Swapping Names and IP Addresses

  • Build new servers with temporary names.
  • Stop the PI Data Archives.
  • Swap the names/IP addresses with the old servers.
  • Restart PI Data Archives on the new hardware.

Pros: No interface reconfiguration required.
Cons: Brief downtime while archives are offline, though buffered data is restored.

 

Option 3: Building a Complete Parallel System

  • Create a fully independent system (Data Archive, AF, interface servers).
  • Restore a backup from the old system.
  • Run both systems in parallel until testing and validation are complete.

Pros: Safest approach, full validation before go-live, allows migration from Batch to Event Frames database in a controlled environment.
Cons: Requires more time, resources, and hardware.

 

Chosen Solution

After carefully reviewing all available upgrade paths, Réalta Technologies determined that Option 2: Swapping Names and IP Addresses was the most effective and efficient solution for this specific project.

 

This decision was based on our team’s in-depth understanding of both the technical requirements and the operational constraints within regulated pharmaceutical manufacturing. By applying our expertise in designing bespoke solutions, we were able to match the upgrade approach precisely to the client’s unique needs.

 

Several factors influenced our decision:

  • Minimising Disruption: The client’s PI System had multiple critical interfaces feeding data from across the site. Reconfiguring and restarting these, as required in other options, would have posed significant risk and extended downtime. Retaining the same names and IP addresses allowed us to transition to the new infrastructure seamlessly without altering the existing interface configuration.
  • Maintaining Operational Continuity: Pharmaceutical manufacturing operates under strict production and compliance demands. Swapping Names and IP Addresses enabled the upgrade to be completed within a tightly controlled maintenance window, avoiding unnecessary interruption to manufacturing activities.
  • Safeguarding Data Integrity: Our approach ensured that all data generated during the brief downtime was buffered and automatically restored to the upgraded system, protecting the accuracy and completeness of production records.
  • Bespoke Problem-Solving: Rather than applying a one-size-fits-all upgrade method, we evaluated the project holistically, balancing efficiency, safety, and compliance. Option 2 offered the best combination of speed, reliability, and risk mitigation for this particular environment.

This tailored solution is a clear example of how Réalta Technologies leverages its deep technical expertise and industry knowledge to deliver results that are both strategically sound and operationally safe, ensuring clients can modernise their systems without compromising productivity or compliance.

 

Benefits of Updating the PI System

  • Modern Infrastructure: New hardware and OS increased system performance and stability.
  • Data Integrity: All historical data retained with no loss during migration.
  • Reduced Operational Risk: Fully tested migration plan ensured predictable results.
  • Regulatory Compliance: Updated system aligned with industry best practices and supported ongoing GMP compliance.
  • Future-Ready Platform: Prepared for integration with advanced analytics, data visualisation tools, and machine learning applications.

 

Conclusion

This PI System upgrade demonstrated Réalta Technologies’ expertise in designing and executing complex data infrastructure projects for regulated industries. By selecting the optimal migration strategy and executing it flawlessly, Réalta ensured the client could continue delivering high-quality pharmaceutical products with minimal disruption.

 

As an AVEVA Endorsed System Integrator, Réalta Technologies provides tailored solutions that modernise industrial data systems while safeguarding compliance, security, and operational excellence.

 

Need help with updating your systems? Get in touch with our team.

Phone: +353 21 243 9113

Email: [email protected] 

PI System Upgrade Case Study for Pharmaceutical Manufacturing | Réalta Technologies

Introduction: 

Réalta Technologies recently completed a critical PI System upgrade for a leading pharmaceutical manufacturer. The project involved replacing ageing hardware and unsupported operating systems, ensuring the client’s data infrastructure met modern performance, security, and compliance standards. 

 

As a trusted partner and AVEVA Endorsed System Integrator, Réalta Technologies delivered a seamless transition to a fully upgraded system without data loss, enabling the client to continue operations without disruption and minimal downtime. 

 

The Challenge

The client’s PI System servers were operating on outdated hardware and operating systems. An upgrade-in-place was not possible due to:

  • Incompatibility with newer OS versions and software.
  • Requirement for new hardware to meet performance demands.
  • The need to keep existing servers intact for rollback in case of an issue.
  • The critical need to avoid any data loss during the transition.

These constraints required a carefully designed migration strategy that balanced operational continuity, data integrity, and validation requirements.

 

Possible Solutions

Drawing on extensive experience in industrial data infrastructure projects, Réalta Technologies identified three potential upgrade strategies:

 

Option 1: Adding New Servers to the PI Collective

  • Build new servers on new hardware and OS.
  • Add them to the existing PI System Collective.
  • Promote one of the new servers to Primary Data Archive.
  • Remove old servers once complete.

Pros: Minimal downtime, data available to users throughout.
Cons: Requires reconfiguring and restarting all interfaces, which can be time-consuming with large systems.

 

Option 2: Swapping Names and IP Addresses

  • Build new servers with temporary names.
  • Stop the PI Data Archives.
  • Swap the names/IP addresses with the old servers.
  • Restart PI Data Archives on the new hardware.

Pros: No interface reconfiguration required.
Cons: Brief downtime while archives are offline, though buffered data is restored.

 

Option 3: Building a Complete Parallel System

  • Create a fully independent system (Data Archive, AF, interface servers).
  • Restore a backup from the old system.
  • Run both systems in parallel until testing and validation are complete.

Pros: Safest approach, full validation before go-live, allows migration from Batch to Event Frames database in a controlled environment.
Cons: Requires more time, resources, and hardware.

 

Chosen Solution

After carefully reviewing all available upgrade paths, Réalta Technologies determined that Option 2: Swapping Names and IP Addresses was the most effective and efficient solution for this specific project.

 

This decision was based on our team’s in-depth understanding of both the technical requirements and the operational constraints within regulated pharmaceutical manufacturing. By applying our expertise in designing bespoke solutions, we were able to match the upgrade approach precisely to the client’s unique needs.

 

Several factors influenced our decision:

  • Minimising Disruption: The client’s PI System had multiple critical interfaces feeding data from across the site. Reconfiguring and restarting these, as required in other options, would have posed significant risk and extended downtime. Retaining the same names and IP addresses allowed us to transition to the new infrastructure seamlessly without altering the existing interface configuration.
  • Maintaining Operational Continuity: Pharmaceutical manufacturing operates under strict production and compliance demands. Swapping Names and IP Addresses enabled the upgrade to be completed within a tightly controlled maintenance window, avoiding unnecessary interruption to manufacturing activities.
  • Safeguarding Data Integrity: Our approach ensured that all data generated during the brief downtime was buffered and automatically restored to the upgraded system, protecting the accuracy and completeness of production records.
  • Bespoke Problem-Solving: Rather than applying a one-size-fits-all upgrade method, we evaluated the project holistically, balancing efficiency, safety, and compliance. Option 2 offered the best combination of speed, reliability, and risk mitigation for this particular environment.

This tailored solution is a clear example of how Réalta Technologies leverages its deep technical expertise and industry knowledge to deliver results that are both strategically sound and operationally safe, ensuring clients can modernise their systems without compromising productivity or compliance.

 

Benefits of Updating the PI System

  • Modern Infrastructure: New hardware and OS increased system performance and stability.
  • Data Integrity: All historical data retained with no loss during migration.
  • Reduced Operational Risk: Fully tested migration plan ensured predictable results.
  • Regulatory Compliance: Updated system aligned with industry best practices and supported ongoing GMP compliance.
  • Future-Ready Platform: Prepared for integration with advanced analytics, data visualisation tools, and machine learning applications.

 

Conclusion

This PI System upgrade demonstrated Réalta Technologies’ expertise in designing and executing complex data infrastructure projects for regulated industries. By selecting the optimal migration strategy and executing it flawlessly, Réalta ensured the client could continue delivering high-quality pharmaceutical products with minimal disruption.

 

As an AVEVA Endorsed System Integrator, Réalta Technologies provides tailored solutions that modernise industrial data systems while safeguarding compliance, security, and operational excellence.

 

Need help with updating your systems? Get in touch with our team.

Phone: +353 21 243 9113

Email: [email protected] 

PI System Upgrade Case Study for Pharmaceutical Manufacturing | Réalta Technologies Read More »

Power BI, Tableau, and SEEQ: Data Visualisation Tools for Modern Manufacturing

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Power BI, Tableau, and SEEQ: Data Visualisation Tools for Modern Manufacturing

Introduction: 

In the age of Industry 4.0, the volume of data generated in manufacturing environments continues to grow exponentially. But data alone doesn’t drive smarter decisions. It’s how you visualise and act on that data that creates real value. For companies in life sciences, pharmaceuticals, and high-volume manufacturing, choosing the right data visualisation tool is critical.

In this blog, we compare three leading tools in the space: Microsoft Power BI, Tableau, and SEEQ, examining their features, benefits, and use cases from the perspective of industrial data analytics.

 

Why Data Visualisation Matters in Manufacturing?

Before diving into the tools, it’s worth revisiting why data visualisation plays such a key role in manufacturing.

Manufacturers face constant pressure to increase yield, reduce downtime, improve compliance, and optimise performance. Data visualisation tools allow plant teams, analysts, and decision-makers to transform raw operational data into actionable insights. Whether tracking equipment efficiency or identifying production bottlenecks, the right dashboard can be the difference between reactive and proactive decision-making.

 

Power BI: Scalable, Accessible, and Microsoft-Native

Microsoft Power BI is one of the most widely used business intelligence platforms in the world. It offers deep integration with Microsoft products, scalability, and user-friendly interfaces, making it a powerful choice for companies already embedded in the Microsoft ecosystem.

 

Key Features:
  • Native integration with Excel, Azure, and SharePoint
  • Drag-and-drop dashboard creation
  • Custom DAX formulas for advanced metrics
  • Scheduled data refresh and real-time dashboards
  • Strong data modelling capabilities
Strengths:
  • Easy to adopt for teams already using Microsoft 365
  • Strong community support and regular updates
  • Affordable pricing tiers at enterprise level compared to other  visualization tools 
  • Suitable for both SME and enterprise scale
Manufacturing Use Cases:
  • OEE Dashboards: Track overall equipment effectiveness across multiple plants
  • Quality Monitoring: Monitor defect rates and identify trends
  • Supply Chain Analysis: Visualise logistics and inventory data
Limitations:
  • Can be less flexible for time-series industrial data
  • Requires additional configuration for integration with industrial historians like AVEVA PI or OSIsoft

Tableau: Powerful Visualisation and Data Exploration

Tableau is known for its visually rich dashboards and ability to handle large datasets from varied sources. It empowers users to explore data intuitively and supports custom, interactive reporting.

 

Key Features:
  • Rich data visualisation capabilities
  • Native support for many data connectors
  • Real-time data exploration and drill-downs
  • Customisable dashboards with dynamic filters
Strengths:
  • Intuitive UI for data analysts and non-technical users
  • Excellent at data storytelling and presenting complex trends
  • Highly flexible for different data sources and schemas
Manufacturing Use Cases:
  • Batch Performance Analysis: Track trends in batch processes over time
  • Energy Consumption Reporting: Visualise and compare energy usage across facilities
  • KPI Reporting Dashboards: Executive-level visual reporting across departments
Limitations:
  • Higher licensing costs than some alternatives
  • Not purpose-built for time-series industrial data
  • More suitable for data analysts than plant-floor users

SEEQ: Purpose-Built for Time-Series Industrial Data

SEEQ is designed specifically for advanced analytics in process manufacturing industries. Built to work with time-series data from historians like AVEVA PI or Canary, SEEQ enables engineers and analysts to gain insights from complex datasets quickly.

Key Features:
  • Native connectivity with AVEVA PI System, OSIsoft, and Canary
  • Purpose-built for time-series and event-based data
  • Predictive analytics and statistical modelling
  • Collaboration features for teams across functions
  • Strong integration with Jupyter for advanced data science
Strengths:
  • Ideal for engineers and process analysts
  • Handles large volumes of industrial data efficiently
  • Designed around manufacturing and life sciences workflows
  • Short time to value with minimal IT setup
Manufacturing Use Cases:
  • Process Optimisation: Identify trends and anomalies in production runs
  • Deviation Analysis: Investigate root causes of failures and off-spec product
  • Batch Comparisons: Compare equipment and material performance across runs
Limitations:
  • Not designed for traditional business metrics (e.g. finance or HR data)
  • Requires familiarity with process data structures and tag naming conventions

 

Choosing the Right Tool for Your Manufacturing Business

The best data visualisation tool depends on your organisation’s needs, data environment, and user base. Here’s a quick comparison:

Tool

Best For

Key Limitation

Power BI

Business dashboards and KPIs

Limited native support for time-series

Tableau

Visual storytelling and data exploration

Cost and complexity for industrial data

SEEQ

Advanced time-series analytics and manufacturing insights

Narrower business use cases

At Réalta Technologies, we work with clients to implement the right data visualisation solution based on their unique needs. This might be AVEVA PI paired with SEEQ for deep process insights, Tableau connected to AVEVA PI for advanced visual storytelling, or Power BI dashboards for plant-wide KPIs and reporting.

 

How Réalta Technologies Adds Value

As experts in industrial data architecture, data science, and automation, Réalta Technologies supports clients through every stage of their data journey. This includes infrastructure and historian setup, advanced analytics, and dashboard delivery.

We’ve successfully delivered SEEQ and AVEVA PI solutions across global manufacturing and life sciences clients. Our partnerships with leading technology providers and our in-house data engineering team ensure solutions that are tailored, validated, and built for real-world impact.

 

Conclusion

Data visualisation is not just about attractive dashboards. It’s about empowering teams with insights. Whether you need plant-level performance metrics, quality trends, or predictive insights, selecting the right visualisation tool is essential.

Power BI, Tableau, and SEEQ each offer distinct advantages. Understanding how they align with your infrastructure, team skillsets, and business goals helps ensure long-term value.

 

Need help selecting or implementing your data visualisation tools? Get in touch with our team.

 

Phone: +353 21 243 9113

Email: [email protected] 

Power BI, Tableau, and SEEQ: Data Visualisation Tools for Modern Manufacturing

Introduction: 

In the age of Industry 4.0, the volume of data generated in manufacturing environments continues to grow exponentially. But data alone doesn’t drive smarter decisions. It’s how you visualise and act on that data that creates real value. For companies in life sciences, pharmaceuticals, and high-volume manufacturing, choosing the right data visualisation tool is critical.

In this blog, we compare three leading tools in the space: Microsoft Power BI, Tableau, and SEEQ, examining their features, benefits, and use cases from the perspective of industrial data analytics.

 

Why Data Visualisation Matters in Manufacturing?

Before diving into the tools, it’s worth revisiting why data visualisation plays such a key role in manufacturing.

Manufacturers face constant pressure to increase yield, reduce downtime, improve compliance, and optimise performance. Data visualisation tools allow plant teams, analysts, and decision-makers to transform raw operational data into actionable insights. Whether tracking equipment efficiency or identifying production bottlenecks, the right dashboard can be the difference between reactive and proactive decision-making.

 

Power BI: Scalable, Accessible, and Microsoft-Native

Microsoft Power BI is one of the most widely used business intelligence platforms in the world. It offers deep integration with Microsoft products, scalability, and user-friendly interfaces, making it a powerful choice for companies already embedded in the Microsoft ecosystem.

 

Key Features:
  • Native integration with Excel, Azure, and SharePoint
  • Drag-and-drop dashboard creation
  • Custom DAX formulas for advanced metrics
  • Scheduled data refresh and real-time dashboards
  • Strong data modelling capabilities
Strengths:
  • Easy to adopt for teams already using Microsoft 365
  • Strong community support and regular updates
  • Affordable pricing tiers at enterprise level compared to other  visualization tools 
  • Suitable for both SME and enterprise scale
Manufacturing Use Cases:
  • OEE Dashboards: Track overall equipment effectiveness across multiple plants
  • Quality Monitoring: Monitor defect rates and identify trends
  • Supply Chain Analysis: Visualise logistics and inventory data
Limitations:
  • Can be less flexible for time-series industrial data
  • Requires additional configuration for integration with industrial historians like AVEVA PI or OSIsoft

Tableau: Powerful Visualisation and Data Exploration

Tableau is known for its visually rich dashboards and ability to handle large datasets from varied sources. It empowers users to explore data intuitively and supports custom, interactive reporting.

 

Key Features:
  • Rich data visualisation capabilities
  • Native support for many data connectors
  • Real-time data exploration and drill-downs
  • Customisable dashboards with dynamic filters
Strengths:
  • Intuitive UI for data analysts and non-technical users
  • Excellent at data storytelling and presenting complex trends
  • Highly flexible for different data sources and schemas
Manufacturing Use Cases:
  • Batch Performance Analysis: Track trends in batch processes over time
  • Energy Consumption Reporting: Visualise and compare energy usage across facilities
  • KPI Reporting Dashboards: Executive-level visual reporting across departments
Limitations:
  • Higher licensing costs than some alternatives
  • Not purpose-built for time-series industrial data
  • More suitable for data analysts than plant-floor users

SEEQ: Purpose-Built for Time-Series Industrial Data

SEEQ is designed specifically for advanced analytics in process manufacturing industries. Built to work with time-series data from historians like AVEVA PI or Canary, SEEQ enables engineers and analysts to gain insights from complex datasets quickly.

Key Features:
  • Native connectivity with AVEVA PI System, OSIsoft, and Canary
  • Purpose-built for time-series and event-based data
  • Predictive analytics and statistical modelling
  • Collaboration features for teams across functions
  • Strong integration with Jupyter for advanced data science
Strengths:
  • Ideal for engineers and process analysts
  • Handles large volumes of industrial data efficiently
  • Designed around manufacturing and life sciences workflows
  • Short time to value with minimal IT setup
Manufacturing Use Cases:
  • Process Optimisation: Identify trends and anomalies in production runs
  • Deviation Analysis: Investigate root causes of failures and off-spec product
  • Batch Comparisons: Compare equipment and material performance across runs
Limitations:
  • Not designed for traditional business metrics (e.g. finance or HR data)
  • Requires familiarity with process data structures and tag naming conventions

 

Choosing the Right Tool for Your Manufacturing Business

The best data visualisation tool depends on your organisation’s needs, data environment, and user base. Here’s a quick comparison:

Tool

Best For

Key Limitation

Power BI

Business dashboards and KPIs

Limited native support for time-series

Tableau

Visual storytelling and data exploration

Cost and complexity for industrial data

SEEQ

Advanced time-series analytics and manufacturing insights

Narrower business use cases

At Réalta Technologies, we work with clients to implement the right data visualisation solution based on their unique needs. This might be AVEVA PI paired with SEEQ for deep process insights, Tableau connected to AVEVA PI for advanced visual storytelling, or Power BI dashboards for plant-wide KPIs and reporting.

 

How Réalta Technologies Adds Value

As experts in industrial data architecture, data science, and automation, Réalta Technologies supports clients through every stage of their data journey. This includes infrastructure and historian setup, advanced analytics, and dashboard delivery.

We’ve successfully delivered SEEQ and AVEVA PI solutions across global manufacturing and life sciences clients. Our partnerships with leading technology providers and our in-house data engineering team ensure solutions that are tailored, validated, and built for real-world impact.

 

Conclusion

Data visualisation is not just about attractive dashboards. It’s about empowering teams with insights. Whether you need plant-level performance metrics, quality trends, or predictive insights, selecting the right visualisation tool is essential.

Power BI, Tableau, and SEEQ each offer distinct advantages. Understanding how they align with your infrastructure, team skillsets, and business goals helps ensure long-term value.

 

Need help selecting or implementing your data visualisation tools? Get in touch with our team.

 

Phone: +353 21 243 9113

Email: [email protected] 

Power BI, Tableau, and SEEQ: Data Visualisation Tools for Modern Manufacturing Read More »

What Is Databricks? A Modern Data Platform for Modern Businesses

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What Is Databricks? A Modern Data Platform for Modern Businesses

Introduction

Databricks is one of the most powerful and versatile platforms available for handling large-scale data analytics, machine learning, and AI workflows. Built on top of Apache Spark, it enables organisations to unify their data and AI strategies with scalable solutions tailored for speed, collaboration, and security.

As industries like life sciences, pharmaceutical manufacturing, and advanced engineering become increasingly data-rich, the need for a platform like Databricks becomes essential. At Réalta Technologies, we use Databricks to help clients unlock real-time insights, streamline operations, and make smarter, faster decisions.

What Is Databricks? 

Databricks is a cloud-based unified analytics platform designed to simplify the process of data engineering, data science, machine learning, and business intelligence. It brings together teams working with data into a single collaborative environment that supports the entire data lifecycle, from ingestion to modelling to visualisation.

It’s often described as a “lakehouse” platform, combining the best features of data lakes (scalability and flexibility) and data warehouses (structured querying and performance) in a single system.

 

 

Key Features of Databricks

 
1. Unified Workspace

Databricks enables data engineers, data scientists, and analysts to work in one collaborative environment. With shared notebooks, version control, and access management, the platform supports streamlined teamwork and knowledge sharing.

 

2. Delta Lake

Delta Lake is an open-source storage layer that brings ACID transaction capabilities to data lakes. This ensures reliability and consistency of data even as it scales.

 

3. Machine Learning & AI Integration

Databricks includes pre-built ML environments, AutoML tools, and native integrations with frameworks like TensorFlow, PyTorch, and XGBoost. This accelerates the development and deployment of machine learning models.

 

4. Optimised Apache Spark Engine

At its core, Databricks runs on Apache Spark, allowing it to process massive datasets quickly and efficiently across multiple nodes.

 

5. Scalability & Cloud Flexibility

Databricks supports multi-cloud environments and allows elastic scaling of compute resources, making it ideal for businesses with variable data workloads.

 

What Are the Benefits of Using Databricks?

Faster Time to Insight: Streamlined data pipelines and real-time processing enable teams to go from raw data to actionable insights faster.

Reduced Data Silos: By centralising your data, teams can eliminate fragmentation across departments and tools.

Improved Collaboration: A single platform for engineering, science, and analytics reduces duplication of work and fosters teamwork.

Scalability: Easily scale your workloads without overhauling infrastructure.

Cost Efficiency: With automated workflows and serverless options, Databricks helps reduce resource waste and manage costs effectively.

Security & Governance: Enterprise-grade controls for access, compliance, and data governance make it suitable for highly regulated industries.

 

Real-World Use Cases

Pharmaceutical Manufacturing

Databricks enables predictive maintenance, process optimisation, and batch analysis by aggregating data from lab systems, MES platforms, and IoT sensors. It supports compliance with regulations like 21 CFR Part 11 through robust audit trails and governance features.

 

Life Sciences R&D

Scientists and analysts can use Databricks to process large-scale genomic or clinical trial data, identify trends, and model outcomes using AI-driven methods.

 

Supply Chain Optimisation

With real-time analytics, Databricks helps monitor production rates, material availability, and logistics to support lean manufacturing strategies.

 

Predictive Quality Control

Machine learning models built in Databricks can detect early warning signs of quality deviations, allowing teams to act before products fall out of spec.

 

How Réalta Technologies Adds Value with Databricks

At Réalta Technologies, our data engineers and data scientists are experts in deploying Databricks to regulated environments. We work closely with clients in life sciences and manufacturing to:

  • Architect and implement secure, scalable Databricks environments.
  • Integrate data sources such as AVEVA PI, SCADA systems, MES, and LIMS.
  • Develop custom machine learning models for anomaly detection, predictive analytics, and process optimisation.
  • Maintain governance and compliance throughout the data lifecycle.
  • Train internal teams on best practices to make Databricks a sustainable part of their operations.

Our partnership with Databricks is a testament to the depth of experience our team brings in leveraging modern platforms to solve complex industrial challenges.

 

Conclusion

Databricks is transforming how industries harness the power of data. With its unified approach to engineering, science, and analytics, it supports innovation, efficiency, and growth at every stage of the data journey.

 

For organisations in regulated sectors, the ability to derive insights while maintaining control and compliance is essential. Réalta Technologies is proud to partner with clients to deliver intelligent, secure, and scalable solutions using Databricks.

 

Need help getting started with Databricks or optimising your existing deployment? Contact Réalta Technologies today:

Phone: +353 21 243 9113

Email: [email protected] 

 

What Is Databricks? A Modern Data Platform for Modern Businesses

Introduction

Databricks is one of the most powerful and versatile platforms available for handling large-scale data analytics, machine learning, and AI workflows. Built on top of Apache Spark, it enables organisations to unify their data and AI strategies with scalable solutions tailored for speed, collaboration, and security.

As industries like life sciences, pharmaceutical manufacturing, and advanced engineering become increasingly data-rich, the need for a platform like Databricks becomes essential. At Réalta Technologies, we use Databricks to help clients unlock real-time insights, streamline operations, and make smarter, faster decisions.

What Is Databricks? 

Databricks is a cloud-based unified analytics platform designed to simplify the process of data engineering, data science, machine learning, and business intelligence. It brings together teams working with data into a single collaborative environment that supports the entire data lifecycle, from ingestion to modelling to visualisation.

It’s often described as a “lakehouse” platform, combining the best features of data lakes (scalability and flexibility) and data warehouses (structured querying and performance) in a single system.

 

 

Key Features of Databricks

 
1. Unified Workspace

Databricks enables data engineers, data scientists, and analysts to work in one collaborative environment. With shared notebooks, version control, and access management, the platform supports streamlined teamwork and knowledge sharing.

 

2. Delta Lake

Delta Lake is an open-source storage layer that brings ACID transaction capabilities to data lakes. This ensures reliability and consistency of data even as it scales.

 

3. Machine Learning & AI Integration

Databricks includes pre-built ML environments, AutoML tools, and native integrations with frameworks like TensorFlow, PyTorch, and XGBoost. This accelerates the development and deployment of machine learning models.

 

4. Optimised Apache Spark Engine

At its core, Databricks runs on Apache Spark, allowing it to process massive datasets quickly and efficiently across multiple nodes.

 

5. Scalability & Cloud Flexibility

Databricks supports multi-cloud environments and allows elastic scaling of compute resources, making it ideal for businesses with variable data workloads.

 

What Are the Benefits of Using Databricks?

Faster Time to Insight: Streamlined data pipelines and real-time processing enable teams to go from raw data to actionable insights faster.

Reduced Data Silos: By centralising your data, teams can eliminate fragmentation across departments and tools.

Improved Collaboration: A single platform for engineering, science, and analytics reduces duplication of work and fosters teamwork.

Scalability: Easily scale your workloads without overhauling infrastructure.

Cost Efficiency: With automated workflows and serverless options, Databricks helps reduce resource waste and manage costs effectively.

Security & Governance: Enterprise-grade controls for access, compliance, and data governance make it suitable for highly regulated industries.

 

Real-World Use Cases

Pharmaceutical Manufacturing

Databricks enables predictive maintenance, process optimisation, and batch analysis by aggregating data from lab systems, MES platforms, and IoT sensors. It supports compliance with regulations like 21 CFR Part 11 through robust audit trails and governance features.

 

Life Sciences R&D

Scientists and analysts can use Databricks to process large-scale genomic or clinical trial data, identify trends, and model outcomes using AI-driven methods.

 

Supply Chain Optimisation

With real-time analytics, Databricks helps monitor production rates, material availability, and logistics to support lean manufacturing strategies.

 

Predictive Quality Control

Machine learning models built in Databricks can detect early warning signs of quality deviations, allowing teams to act before products fall out of spec.

 

How Réalta Technologies Adds Value with Databricks

At Réalta Technologies, our data engineers and data scientists are experts in deploying Databricks to regulated environments. We work closely with clients in life sciences and manufacturing to:

  • Architect and implement secure, scalable Databricks environments.
  • Integrate data sources such as AVEVA PI, SCADA systems, MES, and LIMS.
  • Develop custom machine learning models for anomaly detection, predictive analytics, and process optimisation.
  • Maintain governance and compliance throughout the data lifecycle.
  • Train internal teams on best practices to make Databricks a sustainable part of their operations.

Our partnership with Databricks is a testament to the depth of experience our team brings in leveraging modern platforms to solve complex industrial challenges.

 

Conclusion

Databricks is transforming how industries harness the power of data. With its unified approach to engineering, science, and analytics, it supports innovation, efficiency, and growth at every stage of the data journey.

 

For organisations in regulated sectors, the ability to derive insights while maintaining control and compliance is essential. Réalta Technologies is proud to partner with clients to deliver intelligent, secure, and scalable solutions using Databricks.

 

Need help getting started with Databricks or optimising your existing deployment? Contact Réalta Technologies today:

Phone: +353 21 243 9113

Email: [email protected] 

 

What Is Databricks? A Modern Data Platform for Modern Businesses Read More »

Unified Namespace (UNS)

What Is a Unified Namespace (UNS)? A Guide for Life Sciences and Manufacturing

Blog / By

What Is a Unified Namespace (UNS)? A Guide for Life Sciences and Manufacturing

Introduction

The life sciences and manufacturing industries are facing a common challenge: an overwhelming amount of data scattered across siloed systems, departments, and technologies. Whether it’s sensor readings from the production floor, batch records from MES systems, or operational insights from enterprise platforms, the information exists, but accessing it in a meaningful, unified way is often difficult.

 

This is where the concept of a Unified Namespace (UNS) comes in. While the term has gained visibility in recent years, the core principles behind UNS have existed for decades, with MQTT (Message Queuing Telemetry Transport) being the latest version. As digital transformation continues to shape regulated manufacturing, UNS is fast becoming the backbone of modern industrial data architecture, enabling real-time visibility, simplifying integration, and supporting data-driven decision-making.

Unified Namespace (UNS)

What Is a Unified Namespace?

A Unified Namespace (UNS) is a structured, centralised data layer that brings together real-time information from across an entire organisation  from machines and automation systems on the plant floor to business-level applications in the cloud. It acts as the single source of truth for industrial data, organised in a hierarchical format that mirrors the physical or logical structure of the business.


Unlike traditional architectures that rely on point-to-point integrations or static data lakes, a UNS operates in real-time using event-driven communication. When a change happens on the shop floor, that update is immediately reflected across all connected systems, users, and applications that subscribe to it.


Importantly, the UNS does not store data, it is a live data layer. It acts as the medium through which systems communicate, with data either passed on directly or sent to platforms that handle storage, such as historians or cloud-based analytics systems.


How a Unified Namespace Works

At the core of a UNS is a publish-subscribe model. Instead of pulling data from each system individually, each data source (e.g., a PLC or historian) publishes updates to a central broker. Any authorised system or user can then subscribe to the topics they need, ensuring they always have access to the most current information.


Common protocols used in a UNS include MQTT (Message Queuing Telemetry Transport). MQTT is the most up to date version the most commonly used protocol for implementing a UNS. It is lightweight, efficient, and designed for high-frequency data transmission. Paired with the Sparkplug B specification, MQTT can also handle structured payloads, device state tracking, and session awareness — making it ideal for industrial environments.


The data is typically organised in a logical hierarchy such as:
Enterprise > Site > Area > Line > Machine > Tag


This makes the data not only accessible but easily understandable to humans and machines alike.


Why UNS Matters in Life Sciences and Manufacturing

For life sciences and manufacturing companies, a UNS delivers clear advantages, particularly in environments where traceability, compliance, and timely decision-making are essential.


First, it eliminates data silos, bridging the gap between Operational Technology (OT) and Information Technology (IT). This allows manufacturing, quality, compliance, and business teams to work from a shared, real-time source of data.


Second, it improves data integrity and auditability, crucial in meeting GxP regulations and standards like 21 CFR Part 11 and Annex 11. With time-stamped, structured, and traceable records, regulatory inspections and investigations become far more manageable.


Third, a UNS empowers faster and more accurate decision-making by making the right data available to the right people, in the right format, at the right time, without manual intervention or custom integrations.


Technologies Commonly Used in a UNS

A number of platforms and tools can be used to implement a UNS. These typically fall into three categories: brokers, integration platforms, and data consumers.

 

MQTT Brokers

These act as the central hub where data is published and subscribed to. Popular options include:

  • HiveMQ – A high-performance MQTT broker with robust security and enterprise-grade reliability.
  • Cybus – Designed for industrial environments, Cybus Connectware offers data governance, role-based access control, and secure connectivity.
  • Ignition MQTT Engine (by Inductive Automation) – Frequently used in conjunction with Ignition SCADA, offering full support for Sparkplug B.
MQTT Data Integration Platforms

These platforms help bridge operational systems and higher-level applications, enriching and transforming data as it moves through the UNS.

  • HighByte Intelligence Hub – A powerful industrial data operations platform designed to model, integrate, and flow data in real time between OT and IT systems, supporting both UNS and broader data strategies.
Data Consumers

The UNS itself doesn’t store data — so it must work in tandem with systems that do. This includes:

  • Data historians (like AVEVA PI, Canary, or GE Proficy)
  • Analytics platforms (Power BI, Tableau, cloud services like Azure and AWS)
  • MES, SCADA, and ERP systems that rely on real-time data to manage operations

At Réalta Technologies, we design and implement Unified Namespace architectures using these platforms and more, based on the specific needs, infrastructure, and compliance requirements of each client.


As a newly appointed AVEVA Endorsed System Integrator, Réalta Technologies brings deep expertise in building UNS architectures that are not only technically robust but validated and scalable for regulated environments.

 

The Role of the Data Historian in a Unified Namespace

Although a UNS is not responsible for storing data, data historians play a critical role within this architecture.

A historian provides the long-term storage, analysis, and visualisation capabilities that the UNS layer alone cannot deliver. It collects time-stamped process data from the UNS (or directly from devices), enabling:

  • Batch review and traceability
  • Deviation investigations
  • Regulatory audit readiness
  • Trend analysis and predictive modelling

Platforms like AVEVA PI System, Canary, and GE Proficy Historian are often integrated with UNS architectures to provide robust historical records that complement the UNS’s real-time capabilities.


At Réalta Technologies, we work across these historian platforms, ensuring seamless integration with the UNS and alignment with compliance frameworks in GMP-regulated environments.

 

Key Benefits of Implementing a UNS

Implementing a UNS delivers measurable benefits, including:

  • Real-time, unified access to plant and enterprise data, improving cross-functional collaboration
  • Faster deployment of analytics and machine learning models, as data is structured and accessible
  • Streamlined integration between legacy equipment, modern platforms, and cloud tools
  • Greater agility and scalability, with an architecture that grows with the business
  • Stronger compliance through centralised audit trails and event logging

For companies working in life sciences or regulated manufacturing, the benefits are amplified. Unified access to clean, structured data can dramatically reduce batch review times, improve deviation investigations, and support continuous improvement initiatives, all while maintaining compliance.

 

Considerations for Getting Started

Before implementing a Unified Namespace, companies should consider a few key factors:

  • Current system landscape: Are your automation and IT systems capable of publishing and subscribing to real-time data?
  • Data governance: Who needs access to what data, and what controls are needed?
  • Validation requirements: How will the UNS be documented, qualified, and maintained to meet compliance standards?
  • Scalability: Can the architecture support multiple sites, product lines, or business units?
  • Partner support: Do you have access to integration specialists with experience in building secure, validated UNS environments?

At Réalta Technologies, we offer support from design through deployment, including validation documentation, user training, and long-term managed services.

 

Conclusion

A Unified Namespace is more than a technology trend, it’s a strategic foundation for the future of digital manufacturing. In the life sciences and manufacturing sectors, where the balance between agility, compliance, and performance is critical, a UNS offers a way to unify your data landscape and unlock new value from your systems.

 

By bringing together MQTT brokers, integration platforms like HighByte, and complementary systems like AVEVA PI, a UNS allows organisations to connect their data, and their teams, in a more intelligent way.

 

If you’re considering a Unified Namespace (UNS) or want to explore how it could support your digital strategy, we’re here to help.

Phone: +353 21 243 9113

Email: [email protected]

What Is a Unified Namespace (UNS)? A Guide for Life Sciences and Manufacturing

Introduction

The life sciences and manufacturing industries are facing a common challenge: an overwhelming amount of data scattered across siloed systems, departments, and technologies. Whether it’s sensor readings from the production floor, batch records from MES systems, or operational insights from enterprise platforms, the information exists, but accessing it in a meaningful, unified way is often difficult.

 

This is where the concept of a Unified Namespace (UNS) comes in. While the term has gained visibility in recent years, the core principles behind UNS have existed for decades, with MQTT (Message Queuing Telemetry Transport) being the latest version. As digital transformation continues to shape regulated manufacturing, UNS is fast becoming the backbone of modern industrial data architecture, enabling real-time visibility, simplifying integration, and supporting data-driven decision-making.

Unified Namespace (UNS)

What Is a Unified Namespace?

A Unified Namespace (UNS) is a structured, centralised data layer that brings together real-time information from across an entire organisation  from machines and automation systems on the plant floor to business-level applications in the cloud. It acts as the single source of truth for industrial data, organised in a hierarchical format that mirrors the physical or logical structure of the business.


Unlike traditional architectures that rely on point-to-point integrations or static data lakes, a UNS operates in real-time using event-driven communication. When a change happens on the shop floor, that update is immediately reflected across all connected systems, users, and applications that subscribe to it.


Importantly, the UNS does not store data, it is a live data layer. It acts as the medium through which systems communicate, with data either passed on directly or sent to platforms that handle storage, such as historians or cloud-based analytics systems.


How a Unified Namespace Works

At the core of a UNS is a publish-subscribe model. Instead of pulling data from each system individually, each data source (e.g., a PLC or historian) publishes updates to a central broker. Any authorised system or user can then subscribe to the topics they need, ensuring they always have access to the most current information.


Common protocols used in a UNS include MQTT (Message Queuing Telemetry Transport). MQTT is the most up to date version the most commonly used protocol for implementing a UNS. It is lightweight, efficient, and designed for high-frequency data transmission. Paired with the Sparkplug B specification, MQTT can also handle structured payloads, device state tracking, and session awareness — making it ideal for industrial environments.


The data is typically organised in a logical hierarchy such as:
Enterprise > Site > Area > Line > Machine > Tag


This makes the data not only accessible but easily understandable to humans and machines alike.


Why UNS Matters in Life Sciences and Manufacturing

For life sciences and manufacturing companies, a UNS delivers clear advantages, particularly in environments where traceability, compliance, and timely decision-making are essential.


First, it eliminates data silos, bridging the gap between Operational Technology (OT) and Information Technology (IT). This allows manufacturing, quality, compliance, and business teams to work from a shared, real-time source of data.


Second, it improves data integrity and auditability, crucial in meeting GxP regulations and standards like 21 CFR Part 11 and Annex 11. With time-stamped, structured, and traceable records, regulatory inspections and investigations become far more manageable.


Third, a UNS empowers faster and more accurate decision-making by making the right data available to the right people, in the right format, at the right time, without manual intervention or custom integrations.


Technologies Commonly Used in a UNS

A number of platforms and tools can be used to implement a UNS. These typically fall into three categories: brokers, integration platforms, and data consumers.

 

MQTT Brokers

These act as the central hub where data is published and subscribed to. Popular options include:

  • HiveMQ – A high-performance MQTT broker with robust security and enterprise-grade reliability.
  • Cybus – Designed for industrial environments, Cybus Connectware offers data governance, role-based access control, and secure connectivity.
  • Ignition MQTT Engine (by Inductive Automation) – Frequently used in conjunction with Ignition SCADA, offering full support for Sparkplug B.
MQTT Data Integration Platforms

These platforms help bridge operational systems and higher-level applications, enriching and transforming data as it moves through the UNS.

  • HighByte Intelligence Hub – A powerful industrial data operations platform designed to model, integrate, and flow data in real time between OT and IT systems, supporting both UNS and broader data strategies.
Data Consumers

The UNS itself doesn’t store data — so it must work in tandem with systems that do. This includes:

  • Data historians (like AVEVA PI, Canary, or GE Proficy)
  • Analytics platforms (Power BI, Tableau, cloud services like Azure and AWS)
  • MES, SCADA, and ERP systems that rely on real-time data to manage operations

At Réalta Technologies, we design and implement Unified Namespace architectures using these platforms and more, based on the specific needs, infrastructure, and compliance requirements of each client.


As a newly appointed AVEVA Endorsed System Integrator, Réalta Technologies brings deep expertise in building UNS architectures that are not only technically robust but validated and scalable for regulated environments.

 

The Role of the Data Historian in a Unified Namespace

Although a UNS is not responsible for storing data, data historians play a critical role within this architecture.

A historian provides the long-term storage, analysis, and visualisation capabilities that the UNS layer alone cannot deliver. It collects time-stamped process data from the UNS (or directly from devices), enabling:

  • Batch review and traceability
  • Deviation investigations
  • Regulatory audit readiness
  • Trend analysis and predictive modelling

Platforms like AVEVA PI System, Canary, and GE Proficy Historian are often integrated with UNS architectures to provide robust historical records that complement the UNS’s real-time capabilities.


At Réalta Technologies, we work across these historian platforms, ensuring seamless integration with the UNS and alignment with compliance frameworks in GMP-regulated environments.

 

Key Benefits of Implementing a UNS

Implementing a UNS delivers measurable benefits, including:

  • Real-time, unified access to plant and enterprise data, improving cross-functional collaboration
  • Faster deployment of analytics and machine learning models, as data is structured and accessible
  • Streamlined integration between legacy equipment, modern platforms, and cloud tools
  • Greater agility and scalability, with an architecture that grows with the business
  • Stronger compliance through centralised audit trails and event logging

For companies working in life sciences or regulated manufacturing, the benefits are amplified. Unified access to clean, structured data can dramatically reduce batch review times, improve deviation investigations, and support continuous improvement initiatives, all while maintaining compliance.

 

Considerations for Getting Started

Before implementing a Unified Namespace, companies should consider a few key factors:

  • Current system landscape: Are your automation and IT systems capable of publishing and subscribing to real-time data?
  • Data governance: Who needs access to what data, and what controls are needed?
  • Validation requirements: How will the UNS be documented, qualified, and maintained to meet compliance standards?
  • Scalability: Can the architecture support multiple sites, product lines, or business units?
  • Partner support: Do you have access to integration specialists with experience in building secure, validated UNS environments?

At Réalta Technologies, we offer support from design through deployment, including validation documentation, user training, and long-term managed services.

 

Conclusion

A Unified Namespace is more than a technology trend, it’s a strategic foundation for the future of digital manufacturing. In the life sciences and manufacturing sectors, where the balance between agility, compliance, and performance is critical, a UNS offers a way to unify your data landscape and unlock new value from your systems.

 

By bringing together MQTT brokers, integration platforms like HighByte, and complementary systems like AVEVA PI, a UNS allows organisations to connect their data, and their teams, in a more intelligent way.

 

If you’re considering a Unified Namespace (UNS) or want to explore how it could support your digital strategy, we’re here to help.

Phone: +353 21 243 9113

Email: [email protected]

What Is a Unified Namespace (UNS)? A Guide for Life Sciences and Manufacturing Read More »

Behind the Scenes: Réalta Technologies x Munster Rugby

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Behind the Scenes: Réalta Technologies x Munster Rugby

As proud Official Data Solutions Partners to Munster Rugby, we recently had the opportunity to spend a morning at the Munster Rugby High Performance Centre for a joint photoshoot. It was a great chance to capture some of the people and moments that represent this partnership — from the players and coaching staff to members of the Réalta Technologies team. 

Our work with Munster is built on shared values of performance, precision and continuous improvement, and we’re delighted to continue supporting the team both on and off the field.

You can view some of the shots from the day below.

Behind the Scenes: Réalta Technologies x Munster Rugby

As proud Official Data Solutions Partners to Munster Rugby, we recently had the opportunity to spend a morning at the Munster Rugby High Performance Centre for a joint photoshoot. It was a great chance to capture some of the people and moments that represent this partnership — from the players and coaching staff to members of the Réalta Technologies team. 

Our work with Munster is built on shared values of performance, precision and continuous improvement, and we’re delighted to continue supporting the team both on and off the field.

You can view some of the shots from the day below.

Behind the Scenes: Réalta Technologies x Munster Rugby Read More »