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.

What’s surprising to see today is how business operations struggle to get an integrated view of all business metrics. With greater volumes of data being collected, data analysts just can’t keep up with the pace. This state of affairs alone doesn’t hit as hard as the fact that many in data analytics have just come to accept this situation as a norm and simply bear with this daily struggle.

At GrafanaCon EU, we gathered representatives of the Graphite, Prometheus, InfluxDB, and Timescale projects in the hopes of starting a spirited conversation about the current state of Time Series Databases. They didn’t disappoint! Here are a few highlights from the TSDB panel featuring Erik Nordstrom from Timescale, Dan Cech from Graphite, Paul Dix from InfluxDB, and Tom Wilkie from Prometheus, and moderated by Grafana Labs co-founder and CEO Raj Dutt.

We’ve been on a roll this year with Beelines, our integrations for quick, easy, and automagic instrumentation of your apps. You may have already seen our Node.js, Ruby, and Go beelines – today, we’re excited to announce the release of the Honeycomb Beeline for Python!