Over the years, automation has become a key component in the management of the entire software release lifecycle. Automation helps teams get code from development into the hands of users faster and more reliably. While this principle is critical to your source code and continuous integration and delivery processes, it is equally essential to the underlying infrastructure you depend on.

Containers provide a nifty solution to package up applications along with their dependencies, and for the whole encapsulated process to be run on a host system. This technology is undeniably popular due to its ability to allow developers to create flexible, scalable, reliable solutions in a quicker amount of time. It has enabled more freedom in choosing the technology we use in our applications and has brought development and production environments closer to parity.

This week we’ll learn about the new Google Stackdriver core datasource in Grafana, dive into the new Postgres query editor and share some best practices.

Sifting through all your logs to find what you need can be challenging—especially during an outage, when time is critical and you’re flooded with WARN and ERROR messages. To help you immediately surface useful information from large volumes of logs, we developed Log Patterns.

In Elasticsearch parlance, a document is serialized JSON data. In a typical ELK setup, when you ship a log or metric, it is typically sent along to Logstash which groks, mutates, and otherwise handles the data, as defined by the Logstash configuration. The resulting JSON is indexed in Elasticsearch.