FAQ

We understand why most organizations are trying to maintain an open architecture, in order to remain flexible and avoid vendor lock-in. That’s precisely why we created Pratexo – to allow you to quickly configure, change, and manage your open edge-to-cloud architecture and software stack. What Pratexo helps you design, provision, and manage is entirely open and yours. No black boxes or proprietary software.

The day you decide to stop using Pratexo to help manage your architectures and implementations, nothing changes for you. The systems you create are always yours. But we believe you won’t want to do that because then you will be back to managing such open systems the slow and hard way. Sound too good to be true? Give us just 30 minutes to show you how others are successfully using Pratexo to manage their open architectures today. 

“Edge computing is the practice of processing data at the edge of a network, near the source of the data. In the context of a distributed electrical grid, edge computing can provide several benefits.

“One benefit is that it can help to reduce latency. In a traditional centralized computing model, data must be sent to a central location for processing, which can take time. By processing data at the edge of the network, closer to the source, the time it takes to transmit and process the data can be reduced. This can be especially important in the case of a distributed electrical grid, where real-time data is critical for maintaining stability and reliability.

“Another benefit of edge computing in the context of a distributed electrical grid is that it can help to improve the reliability and resiliency of the system. By distributing computing resources throughout the grid, it is possible to build in redundancies that can help to prevent outages or other disruptions. For example, if one part of the grid goes down, the edge computing resources in that area can help to keep the system running until the issue is resolved.

“Overall, edge computing can help to improve the efficiency, reliability, and resiliency of a distributed electrical grid. It can provide a way to process data closer to the source, reducing latency and improving the timeliness of data-driven decision making. It can also help to improve the reliability and resiliency of the system by distributing computing resources throughout the grid.”

[The artificial intelligence behind ChatGPT gets it – do you?]

The ability to orchestrate and automate the design, deployment, and management of multi-tier, distributed computing infrastructures all the way to the far edge is definitely a new capability that Pratexo is bringing to the market. Historically, only innovative, nimble companies like Pratexo are capable of leading the way in such areas. Without Pratexo, these projects are extremely time-consuming, with an unacceptably high risk of failure.

Having said that, the core technologies that the Pratexo Platform provisions to the edge are some of the most well-established and completely documented components in existence, including technologies and standards like Kubernetes, Kafka, MQTT, MongoDB, and over 100 (and counting) more. These (mostly) open-source technologies have hundreds or even thousands of contributors and are always being improved and evolved (which is another reason that Pratexo is useful to help manage this evolution over time).

The goal of Pratexo is not to replace these mature technologies or newer ones as they are invented – it is to dramatically speed up your ability to stand up and manage systems that use them. Pratexo is not a risky black box, it is a system that generates open edge architectures and micro clouds that you own. Everything is 100% visible and open. You can leave Pratexo at any time and continue to manage your architectures and applications on your own. Of course, we believe you will value the simplification and acceleration that Pratexo brings so highly that you will never want to do that!

In the early days of modern Internet of Things systems, it was often possible to push all data to a central cloud for processing. This is still true today for many IoT pilots and POCs. But what many organizations realize, sooner or later, is that what works at lower volumes of data is no longer workable or cost-effective at higher volumes. Google Trends gives us some data to back this up:

Beginning in 2014, searches for IoT and machine learning began to surge. This was followed approximately 3 years later by a surge in edge computing searches (which is still continuing today). Clearly, those early implementors of IoT and AI systems began to hit some kind of wall for how to process high volumes of data, and are searching for solutions.

AI and machine learning is an area where the ability to process data close to where it is generated is becoming very critical. Central clouds are well-suited for model training but to push gigabytes of video, audio, and raw machine telemetry data to the cloud to run models against, frankly makes no sense.

Then there are issues such as:

• Data privacy: healthcare clients are not able to push hospital sensor data outside of the hospital
• Data security: electrical grid clients are prohibited by law from opening their systems to the public internet
• Connectivity: energy clients operating in difficult environments need to keep their applications and systems running whether or not they have a connection to central systems

For all these reasons, and more, effective IoT (and AI) simply requires the edge. Happily, Pratexo makes orchestrating and automating the design, deployment, and management of multi-tier, distributed computing infrastructures all the way to the far edge as easy as cloud computing is today.

Edge computing is the practice of capturing, processing, and analyzing data (information) close to where it is created, instead from a distant, centralized data-processing center (the ‘cloud’). The “edge” can be located almost anywhere–on a device (like a camera or phone), at an industrial machine, in a vehicle, in a building, or even on a person. Technologies such as artificial intelligence/machine learningand internet of things (IoT) are closely related to edge computing. Some datapoints about edge computing:

• Gartner: “Currently, around 10% of enterprise-generated data is created and processed outsidea traditional centralized data center or cloud. By 2025, Gartner predicts this figure will reach 75%.”
• IDC: “By 2023, more than half of new enterprise IT infrastructure deployed will be at the edge rather than corporate data centers, up from less than 10 percent today, according to IDC. The number of applications at the edge will increase 800% by 2024, IDC predicts.”

For a simple explanation of Pratexo and edge computing, please see this video:

Since it is based on an open-source foundation, Pratexo is architected to be highly compatible with all major cloud providers. In fact, Pratexo can help reduce ‘lock-in’ to any particular cloud provider.

If you have already standardized on the IoT platforms and edge-related technologies of Azure or AWS, you will find that Pratexo enables you to run your applications all the way to the far edge, without necessarily requiring connectivity to central clouds for maintenance and support.

Please contact Pratexo to learn more about how we play well with (and enhance) all the major ‘hyperscaler’ clouds.

5G refers to a wide range of technologies and standards that fundamentally involve the ability to transmit higher volumes of data through radio frequency waves. Some of these technologies run at the ‘telco edge’, often referred to as Multi-Access or Mobile Edge Computing (MEC). As a result, 5G will likely drive the continued explosion in applications and systems that both generate and process ever-increasing volumes of data.

But 5G will not eliminate the need to process data at the far edge, closest to the machines, systems, and sensors that generate it. This is due to factors like cost, speed of data transfer (latency), data security, privacy, or trying to operate in challenging environments where reliably connecting to even the telco edge is not always easy or even possible.

Ultimately, computing at the far edge (with a platform like Pratexo) and 5G technologies must be integrated to ensure complete edge-to-cloud solutions that are reliable, secure, and highly performant.

Distributed computing involved connecting multiple computers together so they can solve more complex challenges than any individual computer could tackle on its own. By distributing these computers near the edge (called ‘edge nodes’), they can form what we call a ‘micro cloud’ on the edge.

Micro clouds running on the edge are highly reliable, scalable and make it possible for inexpensive, low-power computers to tackle even the hardest challenges such as applying artificial intelligence to real-time data and controlling autonomous systems.

IoT platforms generally involve the ability to do basic data processing on the edge, perhaps enabling dashboards and alerts, and then push this IoT data to a central cloud where more complex applications may be hosted. This works in some situations. But, in many cases, factors such as latency, data security, privacy, cost, and the requirement to support fully or partially disconnected systems on the edge make relying solely on this approach impractical.

Pratexo fundamentally enables an organization to run applications at the far edge, including complex systems that incorporate machine learning or artificial intelligence. Pratexo technology is complementary to most IoT platforms, and overcomes the issues listed above. To discuss this further, with respect to any particular IoT platform, please reach out to us.

• Our professional services team can do that work either by ourselves or along with your internal teams.
• We have also formed a team of systems integrators and solution providers that can build on top of Pratexo, include companies like Industry Core in manufacturing, PropTechCore in Smart Buildings, LuxML in defense, Attentec and Matellio for heavy industry are some examples of partners we currently have on board.
• See our Partners page or reach out to us directly so we can help you find the perfect partner.

Ultimately any complex application that needs to process data in real time, at the ‘far edge’ near to where the data is generated is a good candidate to run on Pratexo. Some common application types include:

• Those that make buildings smarter and greener
• Systems that manage self-driving fleets of vehicles of any type
• Manufacturing operations working to improve quality or efficiency
• Solutions that prevent equipment and machines from breaking down
• Connected infrastructure such as power grids and communication networks
• Systems working to improve human safety or security

Pratexo has a specific offering geared towards helping organizations identify and design specific projects to take advantage of edge computing and unlock the value of AI and IoT. Please reach out to us to discuss this further.

There are four characteristics that make Pratexo special:

• Platform enables the rapid configuration and provisioning of custom edge architectures – no such thing as one size fits all on the edge
• Support for complex environments that are fully or partially disconnected from central clouds
• Available as a turnkey, plug and play system including software and hardware
• Future proof: bringing the power of a micro-cloud to the far edge the evolution from basic apps to intelligent systems using AI, without having to start over each time you go to the next level

We sell Pratexo either as software-only, or as a complete plug-and-play solution including edge computing hardware. It is generally sold as a service, and pricing varies based on the number of edge nodes you are running.

Pratexo also does one-off pilots and POCs and holds training courses and workshops. Please contact us directly for more info or to receive a specific project quotation.