Thomas Stringer has a couple of great blog posts on how to understand your Azure monitoring costs and also on how to reduce your costs, see Azure Monitor Log Analytics too Expensive? Part 2 – Save Some Money | Thomas Stringer (trstringer.com). In the past I’ve blogged on How to calculate the Azure Monitor and Log Analytics costs associated with AVD (not an easy task!).

I’ve been contributing to, and creating, Splunk apps for the better part of the last 10 years. But never have I felt more excited to be a Splunk Developer than right now. One of the primary reasons why I am so excited is because of build tools like @splunk/create. At Splunk, we recognize that developers are so crucial to our entire ecosystem.

Spot worker nodes on EKS (Elastic Kubernetes Service) are a great way to save costs by allowing customers to take advantage of unused capacity. With Sumo Logic, we have experimented with and adopted spot worker nodes for some of our EKS clusters to see if we can pass along the same benefits. We decided to share some of the learnings, challenges, and caveats with using spot instances along with the monitoring setup.

You’ve probably seen the term AIOps appear as the subject of an article or talk recently, and there’s a reason. AIOps is merging DevOps principles with Artificial Intelligence, Big Data, and Machine Learning. It provides visibility into performance and system data on a massive scale, automating IT operations through multi-layered platforms while delivering real-time analytics.

The massive shift to remote work makes managing endpoint security more critical and challenging. Yes, people were already using their own devices for work. However, the rise in phishing attacks during the COVID pandemic shows that all endpoint devices are at a higher risk than before. Plus, more companies are moving toward zero-trust security models. For a successful implementation, you need to secure your endpoints.

Security is one of the most changeable landscapes in technology at the moment. With innovations, come new threats, and it seems like every week brings news of a major organization succumbing to a cyber attack. We’re seeing innovations like AI-driven threat detection and zero-trust networking continuing to be a huge area of investment. However, security should never be treated as a single plane.

Hybrid and multi-cloud environments produce a boundless array of logs including application and server logs, logs related to cloud services, APIs, orchestrators, gateways and just about anything else running in the environment. Due to this high volume, logging systems may become slow and unmanageable when you urgently need them to troubleshoot an issue, and even harder to use them to get insights.

Health data is notoriously difficult to collect, route, and transform. I will demonstrate how to leverage the LogStream Observability Pipeline to solve these problems and help users search their Apple Health data.

As a result of the pandemic, we are all navigating an unpredictable mix of virtual, hybrid, and in-person conditions in our business and personal lives. This situation isn’t going away any time soon. The pandemic has prompted businesses across all industries to accelerate their digital transformation initiatives, where the cloud is critical. On-demand self-service environments provide a reason for cloud migration as cloud architectures help businesses reinvent and address uncertainties.

Spot Ocean delivers container-driven autoscaling to continuously monitor and optimize your cloud environment. Positioned at a busy crossroads in the application deployment pipeline, Ocean has a critical role when shipping new containers. Given the highly dynamic nature of Kubernetes environments, events happen constantly and take shape as logs in Ocean. These can help you understand the chain of events in different scaling scenarios, from debugging cluster issues to incident analysis.