We announced Cribl Search in May, and customer reaction has been incredibly positive. We’ve heard for some time that organizations have data everywhere. They have data in their observability lakes, analytics tools, object stores, and at the edge. The big challenge facing enterprises is that existing search models require you to take all of this data that you don’t know is valuable or not, move it into one place, and then make decisions about whether this is valuable?

The Machine Learning team at Splunk has been hard at work over the last several months preparing for a few exciting launches at.conf22, held just a few weeks ago. Splunk customers want to leverage machine learning (ML) in their environments, but many aren’t sure how to use it, or even how to get started.

We’re excited to announce that we’ve recently added Varnish monitoring support for Google Cloud Platform. You can check it out here! Below are steps to get up and running quickly with observIQ’s Google Cloud Platform integrations, and monitor metrics and logs from Varnish in your Google Cloud Platform.

OpenTelemetry (also known as OTel) is a popular open-source framework used to generate telemetry data for traces, metrics, events and logs. In this guide, we are going to cover the best observability and application performance management tools that can be used alongside OpenTelemetry to transform telemetry data into responsive reporting dashboards.

According to the 2021 test automation report, more than 40% of companies want to expand and invest their resources in test automation. While this doesn’t mean manual testing is going away, there is an increased interest in automation from an ROI perspective – both in terms of money and time. After all, we can agree that writing and running those unit test cases are boring.

The Splunk Threat Research Team (STRT) has continued focusing development on the Splunk Attack Range project and is thrilled to announce its v2.0 release with a host of new features. Since the v1.0 release 6 months ago the team has been focused on developments to make the attack range a more fully-featured development testbed out of the box. This blog post will share these additions as well as some of the project’s future directions.

According to recent surveys and reports on the industry, Kubernetes and containers are more popular than ever. Containers and serverless functions are being mainstream and ubiquitous – with a more than 300% increase in container production usage in the past 5 years. This trend is especially true for large organizations, which are often using managed platforms and services.

Network performance monitoring (NPM) and application performance monitoring (APM) are both key pillars of an overall performance and reliability management strategy, especially when dealing with complex, distributed infrastructure across cloud-native environments. NPM and APM also complement each other, in the sense that NPM can serve as an additional source of truth and observability for application performance.

Anomaly detection can be defined by data points or events that deviate away from its normal behavior. If you think of this in the context of time-series continuous datasets, the normal or expected value is going to be the baseline, and the limits around it represent the tolerance associated with the variance. If a new value deviates above or below these limits, then that data point can be considered anomalous.

OpenTelemetry is one of the most fascinating and ambitious open source projects of this era. It’s currently the second most active project in the CNCF (the Cloud Native Computing Foundation), with only Kubernetes being more active. I was at KubeCon Europe last month, delivering a talk on OpenTelemetry and it was amazing to see the full house and the excitement and interest around the project.