The modern edge data stack typically applies to organizations working with unstructured, uncontextualized, unreliable event-based data.
Producing high quality data is not only the key to AI performance, it is the key to succeeding in digital transformation. Managing data quality is...
Customize your edge AI application in these 7 most common places, and use low-code workflows to gain flexibility and agility across multiple teams.
Even though a lot of today’s data is moving to the cloud, there are many cases when data needs to stay at the edge for security and privacy reasons.
The proliferation of edge data models simply means there is a continuous and rapid growth of edge data models. What should enterprises do about it?
Low-code application platforms like Node-RED and Prescient Designer offer easier ways for IT/OT engineers to work with a variety of raw sensor data.
Working with distributed and heterogenous data can be difficult. Optimizing data assets involve working closer to the source of the data.
A distributed data pipeline works with data from multiple sources, in multiple formats, and processes data early to allow for better data insights.
How can companies navigate the rapid growth of distributed data in industrial IoT and edge applications? Balancing the edge and the cloud is key.
When it comes to Industrial Internet of Things (IIoT) adoption, software has become a barrier, not a facilitator. Companies struggle with IIoT...
Our white paper talks about the fundamentals of edge computing and IoT, the challenges of deploying, and how Prescient Designer can help you to...
Prescient Designer makes it easy to partition edge-cloud operations. It creates a powerful framework for users to easily design and optimize their...
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