Edge Computing isn’t DataOps and that distinction matters in Industry.

Choosing a flexible Edge Computing Platform first

Over the past few months, at Barbara we have a recurring theme in conversations with industrial companies: people often confuse the concepts of Edge Computing and DataOps. It’s an easy mix-up as the two are closely related, and in many modern architectures, they work side by side. But they are not the same thing. And understanding the difference is key if you want to build a robust, future-proof data strategy.

In this post we try to explain how each of these layers contributes to industrial digitalization, and highlight why choosing a flexible edge computing platform first, is a smarter long-term move for industrial companies.

With the right Edge Computing platform, you're not locked into a single DataOps vendor like Litmus, Tulip, Kepware, or HighByte. Instead, you gain the freedom to run whichever tools fit each specific use case, or even build your own DataOps layer using open-source stacks. And you can do it all while keeping full control over your infrastructure and your data, and do not limit yourself to basic use cases which does not evolve with the technology.

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With the right Edge Computing platform, you stay vendor-agnostic, fully control your data and infrastructure, and scale beyond basic use cases using the tools—or open-source stacks—that best fit your needs.

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Understanding the Difference: Edge Computing vs. DataOps

Let’s start with the basics.

Edge Computing:

It is about running software applications on-premises, closer to where data is created. Whether that’s a factory floor, an energy substation, or a water treatment plan. Instead of sending raw data to the cloud for processing (with all the latency, cost, and connectivity challenges that come with it), Edge platforms allow you to process and react to data locally. This is critical in industrial environments where milliseconds matter, connectivity isn’t guaranteed, or security and privacy concerns are very real.

DataOps:

On the other hand, it is one specific type of application that focuses on acquiring, cleaning, and delivering data from multiple sources to the right systems, SCADAs, analytics platforms, or cloud-based applications. It plays a vital role in the early stages of the data journey: connecting assets, structuring data, and preparing it for consumption across the organization.

These are two distinct layers: Edge Computing platforms provide the on-premise infrastructure and compute environment at the Edge, while DataOps platforms define how data flows and is managed. Both are important, but for companies aiming to scale into intelligent, autonomous operations, starting with a strong Edge Computing foundation is not just helpful, it’s strategic.

For scaling into intelligent, autonomous operations, a solid Edge Computing foundation isn’t just helpful, it’s strategic. Why is that? Some industrial companies start with DataOps platforms without putting too much attention into the infrastructure they are running in, and this often leads to vendor lock-in and limited flexibility. At Barbara, we believe in keeping those layers clearly separated.

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We don’t access your data, and we don’t dictate which tools you use. Instead, we provide a secure, scalable Edge platform with a powerful industrial Marketplace, that gives you the freedom to run the DataOps tools that best fit each use case. And when you're ready to move beyond data collection into more advanced scenarios, like AI, computer vision, or autonomous control, Barbara Marketplace also equips you with the industrial-grade capabilities to support those next-generation workloads.

Example: Barbara and Litmus

To better illustrate the difference between Edge Computing and DataOps, it helps to compare two prominent platforms in their respective spaces: Barbara (for edge computing) and Litmus (for DataOps).

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At first glance, both platforms operate at the edge and handle industrial data. But their goals, architectures, and strengths are fundamentally different.

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Litmus’s core strength lies in connecting any industrial system such as SCADA, historians, and PLCs, and enabling structured data collection, normalization, and analytics. Over time, Litmus has added container support to allow additional applications to run alongside its core features. However, it lacks several critical infrastructure management capabilities, such as high-availability application clusters, IEC-62443 cybersecurity controls, advanced deployment and configuration tools, workload dashboards, network management, GPU integration, sophisticated container orchestration, and native support for AI model deployment and runtime.

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Barbara, in contrast, is a purpose-built edge computing platform,  designed to run multiple types of applications at the edge with industrial-grade application resiliency, cybersecurity, and usability.

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Using Barbara, several applications can be deployed from Barbara’s Marketplace and private or public repositories. From lightweight data-cleaning scripts to compute-intensive machine learning models that require GPU acceleration. It’s not just about data, it’s about scalable, reliable, and autonomous applications.

So, once Barbara is deployed at the edge, you can run any DataOps stack that fits your needs. Of course you can run Litmus, but you can also integrate complementary or alternative tools like Ignition, Kepware, HighByte, or even deploy custom connectors and logic. With that, Barbara supports more advanced use cases and moves you out from being captive to one single data processing vendor.

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With Barbara, you can deploy any DataOps stack, from lightweight scripts to GPU-powered ML models, giving you the freedom to scale advanced use cases without being locked into a single vendor.

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This openness and flexibility provide organizations with a powerful advantage: you can start small, test vendors, and evolve your stack over time without needing to re-architect your entire software layer. It’s a best-tool-for-the-job approach, ideal for the complex and evolving demands of industrial digitalization.

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Data Ownership, As It Should Be: Yours

A core principle behind Barbara’s architecture is data sovereignty. We believe industrial companies should always retain ownership and control of their data. That’s why Barbara focuses purely on application orchestration and edge infrastructure management and not on analytics. We stay in the control plane. Your data remains yours.

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With Barbara, data stays on your infrastructure and moves only according to your rules. Whether you process it locally, send it to a private cloud, or route it to an external analytics platform, the choice is entirely yours.

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What About Pricing? Let’s Talk about Numbers

Another important consideration when comparing Edge Computing platforms and DataOps tools is pricing models, and we are seeing lately many companies hitting friction they didn’t expect.

Most DataOps platforms, tend to charge based on traffic volume or the amount of data being moved, processed, or streamed. This model can make sense in cloud-native use cases.

But when everything runs on-premises as it does in most industrial environments, it becomes problematic. You’re often paying recurring fees for data that never leaves your site, which quickly adds up and doesn't reflect the actual value being generated. Barbara takes a different approach. We charge based on the number of applications deployed and managed at the edge, not on data volume. This model is far more scalable and predictable for industrial companies.

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Unlike typical DataOps platforms that charge for data volume, Barbara’s pricing is based on deployed applications, making it a more scalable and cost-predictable model for industrial environments.

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As your use cases grow, you only pay for what you manage. This structure is aligned with how industrial systems evolve: incrementally, modularly, and with a clear focus on ROI. It also means your infrastructure cost won’t spike just because you’re collecting more data, which, let’s face it, is the whole point of digital transformation.

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Open Source DataOps? Absolutely

For companies seeking greater customization or just beginning their edge journey, open-source tools provide a powerful and cost-effective alternative, and Barbara makes it easy to deploy and manage them at scale from our Marketplace. 

One popular combination featured in Barbara’s Marketplace is the MING stack, which consists of MQTT, InfluxDB, Node-RED, and Grafana. Together, these tools offer a robust, modular framework for industrial edge applications, but in addition to MING, there’s a growing ecosystem of open-source DataOps frameworks including Kafka, ELK (Elasticsearch, Logstash/Fluentd, Kibana), and others. These alternatives further expand the flexibility and accessibility of modern edge and industrial data operations, and could be a perfect solution for companies willing to be in absolute full control of their data operations. 

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Learn More: Making the MING Stack Edge Deployment Easy with Barbara

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Conclusion

Edge Computing and DataOps are different, and they’re most effective when used together. But by starting with a secure, flexible, and robust edge computing platform like Barbara, industrial companies can create a future-proof foundation that supports any DataOps toolset, avoids vendor lock-in, and is ready to scale into the next generation of intelligent industrial systems.

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