We’re excited to announce that Zendesk is now available as a pre-built content source, along with a host of others, as part of the Workplace Search application. With more than 130,000 customers in 30 countries, Zendesk has become one of the de facto customer service platforms in the world. Each day, millions of users interact with support agents via the cloud-based tool regardless of the support channel they choose.
With Elasticsearch machine learning one can build regression and classification models for data analysis and inference. Accurate prediction models are often too complex to understand simply by looking at their definition. Using feature importance, introduced in Elastic Stack 7.6, we can now interpret and validate such models.
Wherever people encounter a search bar — whether on Google, phone apps, or while shopping online — they're conditioned to expect search experiences that deliver fast and relevant results. With this ever-evolving expectation in mind, millions of developers and organizations have chosen Elasticsearch for building powerful content discovery experiences over the years, to the great delight of their audience and user base.
One of the challenges introduced by microservices architectures is the ability to understand how the application performs and where most time is spent. The Elastic Stack and Elastic APM can provide observability for modern, microservice-based solutions as well as monolithic applications. Application Performance Monitoring (APM) combines different technologies to provide a deep, transparent and holistic view of what each service component is doing, where, when, and for how long.
Application performance monitoring (APM) is a critical part of a unified observability strategy. APM offers deep insights into application performance and behavior, and organizations depend on it to deliver performant and high-quality digital experiences to their customers — both for keeping a proactive pulse on the health of their applications and for troubleshooting issues.
Distributed tracing remains one of the most important features of any tracing system. Nearly a year ago, we announced Elastic APM distributed tracing, let’s take a look at how this useful feature works behind the scenes. Over the past few years, many applications have adopted microservice architecture. Each of the services in a microservice architecture can have their own instrumentation to provide observability into the service.
A lot of businesses are thinking about the future of virtual work and how to make it successful in light of recent world events. For managers and employees used to working in a traditional office space, moving to remote work can be a challenge on many fronts. How do I and my team stay motivated and productive? What’s the best setup for a home office? Most importantly, how do I separate office work from the duties I normally perform around the home?
Security teams must protect attack surfaces that are becoming bigger and more distributed due to the growth of remote work, cloud infrastructure, and other dynamics. These teams understand that meeting this challenge at scale requires the successful incorporation of the appropriate technology into their security operations program.
Distributed tracing is great — it helps you identify (micro)services within complex architectures having issues interfering with user experience, such as high latency or errors. But once a problematic service is identified, it can be difficult to find out which methods are to blame for the slowdown. Well, we have some big news to share for our Elastic APM users within the Java ecosystem.
More than 40 million people use GitHub as a collaboration tool for building software around the world. For most companies — including distributed teams like Elastic — GitHub has become a critical content source for building software, holding much of the information and knowledge upon which the organizations are built, across items like issues, pull requests, and more.
