In the final episode of season two of The Data Chief podcast, we talk with authors of four must-read books for data and analytics leaders — two new and two time-tested. As you invest in your continuous learning, here is the full round up of the latest top books I recommend for today’s data and analytics leaders.
With the growing demands for online communication platforms, the need for testing various scenarios has drastically increased. Therefore when testing WebRTC application implementations most of the time there is a need to provide a video and audio feed to the application to test various aspects of the functionality implementation. To address this need, Loadero was providing several preset audio and video feed configuration combinations.
If you’ve done any reading about service meshes, you’ve probably come across mentions of an open source project named Envoy. And if you’ve done any reading about Envoy, you’ve probably seen references to service meshes. How are these two technologies related? How are they different? Do they work together? I’ll attempt to answer all those questions in this blog post’s first and second parts, plus possibly a few more.
We are happy to announce a new major release of Kuma, and a new major release of Kong Mesh built on Kuma! Kuma 1.3 ships with 10+ new features and countless improvements. Kong Mesh ships we enterprise capabilities for large scale service mesh deployments. We strongly suggest to upgrade, in order to take advantage of the latest and greatest when it comes to service mesh.
Earlier this year, Perforce hosted the Embedded DevOps Summit, exploring solutions to modern challenges in the planning, development, and testing stages of embedded software. As part of this event, Warren Savage, Visiting Researcher at the Applied Research Laboratory for Intelligence and Security (ARLIS) at the University of Maryland, presented Secure Collaboration in Cloud-Based Chip Design Environments.
French multinational automotive manufacturer Renault Group has been investing in Industry 4.0 since the early days. A primary objective of this transformation has been to leverage manufacturing and industrial equipment data through a robust and scalable platform. Renault designed an industrial data acquisition layer and connected it to Google Cloud, using optimized big data products and services that together form Renault's Industrial Data Platform.
