In the previous articles, we covered Devicetree in great detail: We’ve seen how we can create our own nodes, we’ve seen the supported property types, we know what bindings are, and we’ve seen how to access the Devicetree using Zephyr’s devicetree.h API. In this fifth article of the Practical Zephyr series, we’ll look at how Devicetree is used in practice by dissecting the Blinky application.

As a recovering practitioner, one of the things I was always less than thrilled to do was patching. I dreaded having to run updates, ensure everything was configured correctly, and finding windows of time to reboot without knocking an application offline. Just like your cloud workloads — everything changes when it has to be done at scale. Kubernetes has always been built around the DevOps concepts of rapid development, delivery, and iteration.

As we’ve already shared, Elastic is committed to helping OpenTelemetry (OTel) succeed, which means, in some cases, building distributions of language SDKs. Elastic is strategically standardizing on OTel for observability and security data collection. Additionally, Elastic is committed to working with the OTel community to become the best data collection infrastructure for the observability ecosystem.

Starting today, Checkly users can send their traces from synthetics checks to Coralogix to view in-depth synthetic user data along with back-end APM based tracing. This gives SRE’s and Operations engineers a new insight into how the system is responding to automated synthetic tests of your service. For Checkly users, integrating with Coralogix data means it’s easy to correlate end-to-end user experience with backend performance, and track poor performance to its root cause.

At first, the differences between common configuration management tools might seem marginal. But choosing the right solution for automated configuration management can make a huge difference. Some tools will have features that align with your organization’s goals, while others won’t. The choice between Puppet vs. SaltStack is one such decision, and it’s a little more nuanced than you might think.

In the first part of this series, we toured performance APIs in Node.js. We discussed the capabilities of APIs and how they can diagnose slowdowns or network issues in your Node application. Now, in this concluding segment, we will embark on a practical journey, applying these performance hooks in a real-world scenario. You will understand how to effectively use these tools to monitor and enhance your application's performance. Let's dive in and see these concepts in action!

In today's cloud-centric landscape, managing and optimizing cloud resources efficiently is paramount for cloud engineers striving to balance performance and cost-effectiveness. By leveraging solutions like Tines and Elastic, cloud engineering teams can streamline operations and drive significant cost savings while maintaining optimal performance.

Jonny Harris is a WordPress core contributor and committer based in the UK and a member of the WordPress Performance Initiative. This group teams up with Google and Blackfire to optimize the performance of the PHP framework that powers an estimated 43% of the world’s applications. Jonny has been coding with WordPress since 2006 when, at the age of 16, he wanted to start blogging. He gave WordPress a try as it was then and still is today, the leading open-source blogging framework.

Australian business leaders can’t seem to crack the customer experience (CX) code. According to the ServiceNow Customer Experience Intelligence Report, customer service has gotten increasingly worse over the past three years. Australians are waiting longer than ever—spending a combined 107 million hours on hold, 11% more than the previous year.

Traditional database design prioritizes data integrity through normalization. However, for read-heavy workloads, normalized data structures can lead to complex queries and slower performance. Denormalization offers an alternative approach to optimize query execution and improve efficiency. A study concluded that denormalization can improve query performance when implemented with a thorough understanding of application requirements.