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Building Netflix’s Distributed Tracing Infrastructure

Netflix Tech

In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. Now let’s look at how we designed the tracing infrastructure that powers Edgar. This insight led us to build Edgar: a distributed tracing infrastructure and user experience. Stream Processing: to sample or not to sample trace data?

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How Scalable Connectivity Improves Business Agility

Megaport

Building a cloud or data transport strategy? Here’s how scalable connectivity could significantly improve the process – and your overall business agility. If you want to take a deeper look at evaluating your options, take a read of our blog post here. Use cases for scalable connectivity. Day to Day and Project Cycles.

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Unlocking the Power of AI with a Real-Time Data Strategy

CIO

Supply chain With companies trying to stay lean with just-in-time practices, it’s important to understand real-time market conditions, delays in transportation, and raw supply delays, and adjust for them as the conditions are unfolding. This can only be achieved if the underlying data infrastructure is unified, robust, and efficient.

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Navigating Security Challenges Around OT in the DoD’s Manufacturing Lines

Tenable

An ideal OT security solution for the DoD includes features such as: Scalability and flexibility: The solution must be scalable to accommodate different sizes and types of manufacturing facilities within the DoD. This is the fifth blog in our six-part blog series on OT in the DoD.

Energy 63
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How We Define SRE Work, as a Team

Honeycomb

The new SRE charter We believe our work at Honeycomb should fall within the following categories: Champion reliability and scalability Take a long-term, holistic view of the system. This also includes platform-level work around shared infrastructure, tools, and vendors that support SREs’ field of concerns.

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Wipro Delivers Palo Alto Networks Zero Trust OT Security Solutions

Palo Alto Networks

Modernizing the industrial and operational technology (OT) environment has been challenging for customers in verticals like manufacturing, electric utilities, logistics and transportation. The solution is designed to be scalable and adaptable to meet the changing needs of the OT environment.

Network 44
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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. After all, machine learning with Python requires the use of algorithms that allow computer programs to constantly learn, but building that infrastructure is several levels higher in complexity.