Metrics help you understand how your business and applications are performing. Longer metric retention enables quarter-over-quarter or year-over-year analysis and reporting, forecasting seasonal trends, retention for compliance, and much more. We recently announced the general availability (GA) of extended metric retention for custom and Prometheus metrics in Cloud Monitoring, increasing retention from 6 weeks to 24 months. Extended retention for custom and Prometheus metrics is enabled by default.

Elastic Cloud Enterprise (ECE) makes it easy to manage your Elastic Stack deployments, just like role-based access control (RBAC) makes it easy to manage your users. Combining the two can really make an administrator's life much simpler. The intent of this blog post is to provide instructions for configuring a SAML realm for RBAC in ECE environments where Auth0 is used as an identity provider (IdP).

Once you’ve reached the point where you want to deploy your machine learning models to production, you will eventually need to monitor operations and performance. You might also want to receive alerts in case of any unexpected behavior or inconsistencies with your model or your data quality. This is where you most likely start learning about various aspects of Machine Learning Operations (MLOps).

Java is a well-established object-oriented programming language that epitomizes cross-platform software development and helped to popularize the “write once, run anywhere” (WORA) concept. Java runs on billions of devices worldwide and powers a huge range of important software, such as the popular Android operating system and Elasticsearch. In this tutorial, we will go over how to manage Java logs with the ELK Stack and Logz.io.

In our blog post on structuring Elasticsearch data with grok on ingest for faster analytics, we took a look at how to structure unstructured data on ingest (schema on write) to make sure your analytics run at near real time. Speed like that can help take your observability use cases to the next level. In this article, we’re going to build on what we learned by incrementally creating a new grok pattern from scratch!

Open source grafana is one of the most popular OSS UI for metrics and infrastructure monitoring today. Capable of ingesting metrics from the most popular time series databases, it’s an indispensable tool in modern DevOps. This OSS grafana tutorial will go over installation, configuration, queries, and initial metrics shipping. Open source grafana is the equivalent of what Kibana is for logs (for more, see Grafana vs. Kibana).

Being able to easily monitor the health of all your sites and services from multiple global locations is a powerful tool for site reliability. However, no one wants to sit and stare at a status dashboard all day. Naturally, teams want to be alerted when there is an issue. We can do that with alerting in Kibana. And when coupled with Elastic machine learning, alerts can be automatically generated from anomalies that are automatically detected. That’s the power of Elastic Observability.

We examined best practices for logging in a prior series. However, how can you apply those best practices in real life? Let’s dive into how you could use LogDNA in an opinionated manner to utilize best practices to bring value to your DevOps-focused projects. How can we ensure we follow best practices and keep our logs secure and compliant as noted in the previous series? Let’s pretend we’re setting up centralized log management with LogDNA for a new team and project.