In this guest blog post from the folks at Ballerina, Anjana shows you how you can easily visualize metrics from a Ballerina service with Grafana, walking you step by step through the installation and configuration of the components. They’ve also extended an offer for a free ticket to their upcoming Ballerinacon to the Grafana community.

Traditional logging solutions require teams to provision and pay for a daily volume of logs, which quickly becomes cost-prohibitive without some form of server-side or agent-level filtering. But filtering your logs before sending them inevitably leads to gaps in coverage, and often filters out valuable data.

In our previous article (How to Scale and Manage Millions of Metrics), we looked at correlations in terms of name similarity, but there are other types of similarities that occur between metrics.

In our previous post about Honeycomb Tracing, we used tracing to better understand Honeycomb’s own query path. When doing this kind of investigation, you typically have to go back and forth, zooming out and back in again, between your analytics tool and your tracing tool, often losing context in the process.

Elasticsearch 6.3 included some major new features, including rollups and Java 10 support, but one of the most intriguing additions in this version is SQL support.

Juraj Kosik, an Infrastructure Security Technical Lead at Deutsche Telekom Pan-Net, has written a detailed case study of how his organization implemented Graylog to centralize log data from multiple data centers exceeding 1 TB/day. His case study provides thorough insights into real-world issues you might run into when implementing and operating a log management platform in a large-scale cloud environment.

The amount of data being generated today is unprecedented. In fact, more data has been created in the last 2 years, than in the entire history of the human race. With such volume, it’s crucial for companies to be able to harness their data in order to further their business goals. A big part of this is analyzing data and seeing trends, and this is where solutions such as Graphite and Grafana become critical.

When we released derived columns last year, we already knew they were a powerful way to manipulate and explore data in Honeycomb, but we didn’t realize just how many different ways folks could use them. We use them all the time to improve our perspective when looking at data as we use Honeycomb internally, so we decided to share. So, in this series, Honeycombers share their favorite derived column use cases and explain how to achieve them.