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Introducing Grafana Beyla: open source ebpf auto-instrumentation for application observability

Do you want to try Grafana for application observability but don’t have time to adapt your application for it? Often, to properly instrument an app, you have to add a language agent to the deployment or package. And, in languages like Go, proper instrumentation means manually adding tracepoints. Either way, you have to redeploy to your staging or production environment once you’ve added the instrumentation.

Remote Desktop Integrations: Connect InvGate Insight to TeamViewer, Windows Remote Desktop, AnyDesk, And VNC

If you’re looking to integrate your IT Asset Management tool with a remote desktop application to streamline your support capabilities, you’ve come to the right place. InvGate Insight easily connects with the most popular options out there: TeamViewer, Windows Remote Desktop, AnyDesk, and VNC. And we’re about to show you exactly how in the next lines. So, stay tuned!

AWS Savings Plans Vs. Reserved Instances: When To Use Each

A decade after launching Reserved Instances (RIs), Amazon Web Services (AWS) introduced Savings Plans as a more flexible alternative to RIs. AWS Savings Plans are not meant to replace Reserved Instances; they are complementary. SPs and RIs have some significant differences that make each better suited to specific uses. As an example, while Savings Plans are applicable to both EC2 and Fargate instances, RIs are only applicable to EC2 instances. This guide will cover.

VMware Tanzu Service Mesh Named a Leader in 2023 GigaOm Radar Report on Service Mesh

GigaOm has once again placed VMware Tanzu Service Mesh within the leader ring of its Radar Report on Service Mesh. This year Tanzu Service Mesh has been upgraded to the Outperformer label, moving closer to the center and marking its heightened recognition as an industry leader. This is not only a testament to our robust enterprise capabilities and broad support for various application platforms, public clouds, and runtime environments, but also a validation of our strategic approach.

Amazon RDS: managed database vs. database self-management

Amazon RDS or Relational Database Service is a collection of managed services offered by Amazon Web Services that simplify the processing of setting up, operating, and scaling relational databases on the AWS cloud. It is a fully managed service that provides highly scalable, cost-effective, and efficient database deployment.

Deploying Single Node And Clustered RabbitMQ

RabbitMQ is a messaging broker that helps different parts of a software application communicate with each other. Think of it as a middleman that takes care of sending and receiving messages so that everything runs smoothly. Since its release in 2007, it's gained a lot of traction for being reliable and easy to scale. It's a solid choice if you're dealing with complex systems and want to make sure data gets where it needs to go.

Monitor the health of your Temporal Server with Datadog

Temporal is an open source programming model that enables users to write and run scalable and reliable cloud applications. The Temporal Platform consists of a Temporal Cluster and Worker Processes, which together create a runtime for reentrant processes called Workflow Executions. Temporal’s workflows are resilient programs that execute tasks and react to external events, including timers and signals.

Effective Logging in Threaded or Multiprocessing Python Applications

In Python development, logging is not only good practice; it is vital. Logging is critical for understanding the execution flow of an application and helps in debugging potential issues. The importance of logging for developing reliable and maintainable Python applications cannot be overstated. Python provides capabilities for running concurrent operations—either in a threaded (single process) or multiple process environment. But what implications do these different approaches have on logging?

Our first ML based anomaly alert

Over the last few years we have slowly and methodically been building out the ML based capabilities of the Netdata agent, dogfooding and iterating as we go. To date, these features have mostly been somewhat reactive and tools to aid once you are already troubleshooting. Now we feel we are ready to take a first gentle step into some more proactive use cases, starting with a simple node level anomaly rate alert. note You can read a bit more about our ML journey in our ML related blog posts.