Elastic announced new features and updates across the Elastic Observability solution in its 7.13 release to streamline workflows in Microsoft Azure, simplify data integrations, and accelerate root cause analysis.
Expanded capabilities include native integration in the Microsoft Azure console, the beta release of Fleet Server, and new troubleshooting views in Elastic APM.
Elastic is announcing an enhanced partnership with Microsoft, enabling users to find and deploy Elastic directly from the Azure console and natively integrate observability and security data from Azure services. In Elastic Observability, the new native Azure console integration allows customers to easily onboard logs and metrics from their Azure services. This includes both compute services, such as virtual machines and containers, and non-compute services, such as Azure SQL Database and Azure Data Factory. Users can easily configure their setups with tag-based filters to limit data collection to only specific resources.
Elastic also announces the beta release of Fleet Server, a new app in Kibana that allows practitioners to centrally manage an entire fleet of Elastic Agents. Fleet Server offers a distributed architecture for scalability and flexible deployment. It can be deployed either centrally or close to Elastic Agents. Together, the Azure console integration and Fleet Server significantly lower total cost of ownership and time to value for users of Elastic Observability.
New enhancements to the Elastic APM Service Overview page are now available with time comparison and enhanced APM service instance views, enabling users to further accelerate root cause analysis and lower mean-time-to-resolution (MTTR). The time comparison view allows users to quickly and directly compare current and historical behavior, while the scatterplot view displays instances by latency and load distribution to reveal instances that are behaving differently under load. An enhanced instance panel breaks down services by instance, providing per-instance metrics and trends to quickly identify which instances might be contributing to service issues.
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...