Operations | Monitoring | ITSM | DevOps | Cloud

One of the unintended side-effects of the public cloud is the enablement of a platform-agnostic cloud architecture. This means that it’s now easier than ever to mix offerings from multiple vendors (cloud vendors in this case) to form a single application or service-based solution. There is one gotcha, however: log management.

Many of our users are responsible for monitoring logs to detect sudden changes in volume or to control the budget. To help you with this goal, we’re excited to announce new enhancements to our usage dashboard.

As Elasticsearch is gradually becoming the standard for textual data indexing (specifically log data) more companies struggle to scale their ELK stack. We decided to pick up the glove and create a series of posts to help you tackle the most common Elasticsearch performance and functional issues. This post will help you in understanding and solving one of the most frustrating Elasticsearch issues – Mapping exceptions.

Elasticsearch, Logstash, and Kibana — the trio better known as Elastic Stack (or ELK, if you prefer a term that is now going out of style), make up a powerful set of tools for searching and analyzing data. Their power derives not just from their technical features, but also the fact that Elastic Stack is an open source platform that anyone can download and set up anywhere.

Elastic Stack Features (formerly X-Pack) is an Elastic Stack extension that bundles security, alerting, monitoring, reporting, and graph capabilities. One could use either all or specific components.

C# logs can be an invaluable resource for optimizing application performance and debugging errors. But it’s not easy to extract the full potential of your logs if they’re not providing enough context around each error, or if they’re written in a format that’s difficult to parse.

As applications are getting more complex, it’s becoming harder to deliver high-quality applications. Tools likeJavaScript has come a long way in recent years. Browsers are becoming more robust and machines are growing more powerful. Pair this with the recent development of Node.js for execution of JavaScript on servers, and you can understand why JavaScript has exploded in popularity.

The days when you could simply SSH into a server and perform a fancy grep are long gone. If you’re reading this article, chances are either you are looking to move from that obsolete approach to a centralized logging approach with a log management tool, or you are looking for an alternative log management tool to replace your existing solution. Problem is, there are so many different tools out there, making a choice can be overwhelming. So how do you pick the right solution?