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We're excited to share that Checkly has been named a 2023 Winter Intellyx Digital Innovator. This recognition resonates deeply with our Monitoring as Code (MaC) workflow and the values we uphold in delivering Checkly to cloud-native engineers, solving uptime and reliability challenges to ship with confidence.
Tracealyzer version 4.8.2 has just been released. This version mainly fixes bugs, such as custom state machine models not being remembered on trace reload, and eliminates a number of compiler warnings in the Recorder source code. In addition, the update features improved streaming over UDP, and the bundled SSH library SSH.NET has been updated to the latest version. Users with a current maintenance contract can upgrade to Tracealyzer 4.8.2 from within the application, or by visiting the update page.
We’re happy to announce that we now offer a free trial of our VictoriaMetrics Enterprise solution! Designed to help solve an organisation’s monitoring and observability set ups, no matter the scale, VictoriaMetrics Enterprise provides reliable, secure and cost-efficient monitoring. The free trial of VictoriaMetrics Enterprise is perfect for organisations with large data loads, for whom cost-efficient monitoring is mission-critical.
Cloud-native developers and practitioners gathered from around the world to learn, collaborate, and network at KubeCon/CloudNativeCon North America 2023 between November 6th and 9th at McCormick Place in Chicago, IL—myself included. This wasn’t my first time attending—I’ve been coming to KubeCon since 2016—but it was easily one of the most exciting experiences I’ve had as part of the Cloud Native community.
In the dynamic world of IT, traditional network monitoring approaches are no longer sufficient to manage the complexities of today’s networks—be they wired or wireless. To stay ahead of network events, IT administrators must shift from being reactive to adopting a proactive stance. This transition involves a comprehensive approach to network monitoring that includes forecasting future network requirements with the help of machine learning (ML) technology.