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.
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.
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.
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.
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.
Love it or hate it, many organizations have Microsoft Windows as part of their infrastructure. They usually operate a series of Windows services like: Although surveys report that the market share of businesses using Windows is smaller than that of businesses using Linux, many organizations still use private Windows servers that are not accessible over the internet.
Because DevOps practices can bring great speed and reliability to the software delivery lifecycle, release management can seem daunting. But, the improved visibility and collaboration brought about by DevOps can also help with the release management process. DevOps-centric release management is the future of software development and IT operations.
Since the evolution of the IT industry, different concepts have been introduced to enhance and speed application production. Automating processes is gradually becoming the way forward and, so far, the best way to speed the deployment process of projects. Today, though, NoOps has come along. The prevalence of NoOps means manual intervention may not be needed in IT operations, but is this going to mean the extinction of DevOps? Turns out, NoOps might just be a next step in the progression of DevOps.
Whether you are new to Splunk or just needing a refresh, this post can guide you to some of the best resources on the web for using Splunk. We’ve gathered, in a single place, the tutorials, guides, links, and even books to help you get started with Splunk.
DevOps-oriented engineers live at the intersection of IT operations and software development: understanding much of what it takes to maintain IT infrastructure while also being able to write code and deploy new services. DevOps-minded teams not only create services — they also maintain them. A DevOps structure forces teams to take accountability for their applications and infrastructure instead of allowing developers to throw code over the proverbial wall to IT operations.
