Cribl has a unique position right in the middle of the observability market, giving us a distinct view of all things security, APM, and log analysis. Observability as a concept has exploded into specialized areas over the past two years, and making sense of the players and market forces, particularly in a difficult macro environment, can be tricky. Let’s break it down.
Fintech companies operate in a complex technological and regulatory environment. They rely heavily on cloud-native technologies and microservices architectures to handle financial transactions and data, often at a massive scale. To maximize application reliability, fintech companies need full visibility into their software systems and applications. An agile monitoring solution like observability is crucial to improving performance and user experience.
In this article, we will deploy a clustered Prometheus setup that integrates Thanos. It is resilient against node failures and ensures appropriate data archiving. The setup is also scalable. It can span multiple Kubernetes clusters under the same monitoring umbrella. Finally, we will visualize and monitor all our data in accessible and beautiful Grafana dashboards.
Grafana is a powerful open-source visualization solution that provides valuable insights into the performance of infrastructure, applications, and servers. With customizable visualizations and support for diverse data sources and formats, Grafana allows IT teams to collect and visualize data from various sources.
We recently launched several new Cloud Monitoring features to improve your visualization and troubleshooting experience.
Log Management tools are crucial for the security and performance of your IT infrastructure. With the right log management system, you can quickly detect and respond to any anomaly or performance issue. Presently, there are numerous log management platforms. Each with its own unique set of features and benefits. While most of these platforms offer industry-standard capabilities, what sets them apart from each other are the stand-out features, pricing, and overall user experience.
If you work with large amounts of log data, you know how challenging it can be to analyze that data and extract meaningful insights. One way to make log analysis easier is to normalize your log messages. In this post, we’ll explain why log message normalization is important and how to do it in Graylog.
