In our latest comparison guide for 2023, we'll cover all of the best IT infrastructure monitoring software that you should consider using to maintain uptime and improve your system’s performance.

Dashboards are powerful tools for monitoring and troubleshooting your system. Too often, however, we run into an incident, jump to the dashboard, just to find ourselves drowning in endless data and unable to find what we need. This could be caused not just by the data overload, but also due to seeing too many or too few colors, inconsistent conventions or the lack of visual cues.

Whether you’re a DevOps, SRE, or just a data driven individual, you’re probably addicted to dashboards and metrics. We look at our metrics to see how our system is doing, whether on the infrastructure, the application or the business level. We trust our metrics to show us the status of our system and where it misbehaves. But do our metrics show us what really happened? You’d be surprised how often it’s not the case.

If you’re just getting familiar with full-stack observability and Coralogix and you want to send us your metrics and traces using the new OpenTelemetry Community Demo Application, this blog is here to help you get started. In this simple, step-by-step guide, you will learn how to get telemetry data produced by the OpenTelemetry Demo Webstore into your Coralogix dashboard using Docker on your local machine.

Another day starting up your laptop or workstation, logging into programs, and waiting for that first call to come in. As an IT help desk analyst, you love when you can solve people’s problems, but sometimes the number of calls feels overwhelming. Although each analyst tier responds to different customer or employee concerns, you all share the same basic job functions like answering calls, asking questions, and research answers.

A new year means a new set of goals. In 2022, we set some lofty goals to help our customers achieve clarity across their modern IT infrastructure. We set out to do this by improving our log collection and analysis within LM Envision, our unified observability platform, which was announced at LogicMonitor’s Elevate user conference this summer. At the conference, we gathered feedback to understand the various ways our customers access and review log data.

In the world of observability, there are several distinct problems to solve. Fast queries, intuitive visualizations, scalable storage, and more. The technical problems receive the most attention; however, there is another, more subtle problem. How do observability platforms facilitate collaboration on the scale needed by organizations?

One tool for all your Python performance tracking needs We're building this neat service in Python to ingest data in Elasticsearch from various sources (MySQL, Network Drive, AWS, etc.) for Enterprise Search. Sucking data from a third-party service to Elasticsearch is usually an I/O-bound activity. Your code sits on opened sockets and passes data from one end to the other. That's a great use case for an asynchronous application in Python, but it needs to be carefully crafted.

Client logging refers to the practice of collecting and storing log messages generated by client software, such as a web browser or mobile application. These log messages can provide valuable information about the behavior and performance of the client software, as well as any errors or issues that may have occurred. Client logging is often used by developers to troubleshoot and debug software issues, as well as to gather data for analysis and performance optimization.

Logging as a service (LAAS) is a type of cloud computing service that allows organizations to store and manage their log data in a central location. This type of service typically includes features such as centralized storage, real-time analytics, and search capabilities, as well as tools for visualizing and analyzing log data. Logs help you debug and troubleshoot your applications. They are also useful for other purposes like auditing and compliance, performance monitoring, and security.