Log management costs are growing, and it's a concern for companies, users, and developers trying to scale their organizations in today’s macro environment. Companies are making investments in systems that collect data from the cloud, applications, and infrastructure in order to monitor their performance and security. The amount of machine data generated every day is skyrocketing as businesses digitize and automate operations.

New Relic is a huge name in the website observability and analytics industry. They’ve carved out a space for themselves in a highly competitive monitoring space, and have garnered thousands of users and hundreds of millions in revenue. New Relic is known for its Infrastructure Monitoring capabilities, but it also has a number of other tools that are just as popular. But, New Relic is not so popular with everyone.

If you’re thinking of running OpenSearch on Kubernetes, you have to check out the OpenSearch Kubernetes Operator. It’s by far the easiest way to get going, you can configure pretty much everything and it has nice functionality, such as rolling upgrades and draining nodes before shutting them down. Let’s get going 🙂

You want your networks to operate seamlessly, but how can you guarantee that your network is performing optimally and without disruptions? Network monitoring can help. Network monitoring means overseeing a network's performance, availability, and overall functionality — allowing you to identify and resolve issues before they impact end-users. Read on for a full understanding.

DevOps is a practice that combines software development and IT operations to improve the speed, quality, and efficiency of software delivery. By breaking down traditional silos between development and operations teams and promoting a culture of continuous improvement, DevOps helps organizations achieve their goals and remain competitive in today’s fast-paced digital landscape. To better understand how we asked engineers what key DevOps benefits they noticed since working with this approach.

In this tutorial, we will learn about configuring Filebeat to run as a DaemonSet in our Kubernetes cluster in order to ship logs to the Elasticsearch backend. We are using Filebeat instead of FluentD or FluentBit because it is an extremely lightweight utility and has a first-class support for Kubernetes. It is best for production-level setups. This blog post is the second in a two-part series. The first post runs through the deployment architecture for the nodes and deploying Kibana and ES-HQ.

This is the first post of a 2 part series where we will set up production-grade Kubernetes logging for applications deployed in the cluster and the cluster itself. We will be using Elasticsearch as the logging backend for this. The Elasticsearch setup will be extremely scalable and fault-tolerant. ‍

OpenTelemetry (OTel) is steadily gaining broad industry adoption. As one of the major Cloud Native Computing Foundation (CNCF) projects, with as many commits as Kubernetes, it is gaining support from major ISVs and cloud providers delivering support for the framework. Many global companies from finance, insurance, tech, and other industries are starting to standardize on OpenTelemetry.

Loki is a open source log aggregation tool developed by Grafana labs. It is inspired by Prometheus and is designed to be cost-effective and easy to operate. But Loki also has some limitations, and you might want to explore some Loki alternatives for your log analytics. In this article, we will look at 11 log management tools you can use as a Loki alternative. Loki is designed to keep indexing low. It does this by making use of labels.

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?