In this post, I want to scratch at the surface of a very interesting technology that Elastic’s Universal Profiler and Security solution both use called eBPF and explain why it is a critically important technology for modern observability. I’ll talk a little bit about how it works and how it can be used to create powerful monitoring solutions — and dream up ways eBPF could be used in the future for observability use cases.

This year, global ecommerce transactions are expected to grow by over 12% during the 2022 holiday season. As the industry continues to rely more on ecommerce, retailers are looking at new ways to improve customer experience and provide a safe and secure shopping journey. In an increasingly competitive space, many retailers are leveraging their data assets to accomplish more, spark innovation, create more personalized experiences, and drive higher conversion rates.

An avid bird-watcher once told me that for bird-watching beginners, it’s more important to focus on learning about the birds and identifying their unique songs rather than trying to find the perfect pair of binoculars.

A few months back, we introduced the beta release of Elastic APM.NET agent profiler auto-instrumentation. Fast forward to today, we're excited to announce the general availability (GA) of this powerful capability that allows the.NET APM agent to automatically instrument.NET Framework, .NET Core, and.NET applications without requiring code changes or recompilation.

The latest release of Elastic Enterprise Search introduces a suite of new features and capabilities for building world-class search experiences for your mission-critical applications, websites, online stores, or anything in-between: With this release, building search experiences for your ecommerce retail site, enhancing your employees’ ability to access relevant HR documentation, or building a custom application to quickly draw analytic insights can draw on the power of using the trained model of

As an operations engineer (SRE, IT manager, DevOps), you’re always struggling with how to manage technology and data sprawl. Kubernetes is becoming increasingly pervasive and a majority of these deployments will be in Amazon Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), or Azure Kubernetes Service (AKS). Some of you may be on a single cloud while others will have the added burden of managing clusters on multiple Kubernetes cloud services.

I like to leverage our technologies to ensure our products have a pleasant user experience. Elastic Synthetics enables you to configure it in an out-of-the-box experience directly through your Elastic Cloud deployment without the need to install anything! It also works across the globe with multiple locations you can choose from. Ever wondered how fast your web service is when accessed from Japan, Germany, or the eastern U.S.? Now you can do this by simply clicking on a checkbox.

Hopefully by now you’re taken your first sip of Elastic RUM, or real user monitoring, and see the power of searching through traces and the User Experience metrics to gain insights into how users actually use and experience your application. One issue you may have experienced is the challenge of finding the source of errors for minified JavaScript files.

Creating personalized search experiences can be challenging. In this post, we’ll demystify the steps to get started, so you can prioritize search results according to user profiles, offer relevant recommendations, and accelerate workflows. But before we get to that, let’s address why personalized search matters.

In this article, we'll explore the concepts of variants and SKUs in ecommerce, and how to best handle these when modeling data for your ecommerce search experiences. We're optimizing our models using Elastic Enterprise Search.