Operations | Monitoring | ITSM | DevOps | Cloud

Businesses are increasingly turning to cloud computing to drive innovation, scalability, and cost efficiencies. For many, managing cloud costs becomes a complex and daunting task, especially as organizations scale their cloud infrastructure and workloads. In turn, cloud cost management tools can help teams gain better visibility, control, and cost optimization of their cloud spending. These tools not only provide comprehensive solutions to track and analyze, they also optimize cloud expenses.

IDC published a Market Perspective report discussing implementations to leverage Generative AI. The report calls out the Elastic AI Assistant, its value, and the functionality it provides. Of the various AI Assistants launched across the industry, many of them have not been made available to the broader practitioner ecosystem and therefore have not been tested. With Elastic AI Assistant, we’ve scaled out of that trend to provide working capabilities now.

A robust observability strategy forms the backbone of a successful cloud environment. By understanding cloud observability and its benefits, businesses gain the ability to closely monitor and comprehend the health and performance of various systems, applications, and services in use. This becomes particularly critical in the context of cloud computing. The resources and services are hosted in the cloud and accessed through different tools and interfaces.

Node.js logging, like any form of software instrumentation, isn’t an easy thing to get right. It takes time, effort, and a willingness to continue to iterate until a proper balance is struck. There are so many points to consider, including: Previously, here on the Loggly blog, I began exploring these questions in the context of three of the most popular web development languages: PHP, Python, and Ruby. But these aren’t the only popular languages in use today.

In the context of application performance monitoring (APM) and observability, traces and spans are fundamental concepts that help users to track and understand the flow of requests and operations within a system. They are essential in assisting users to identify bottlenecks, troubleshoot issues, and optimize application performance.

For today’s IT and security professionals, threats come in many forms – from external actors attempting to breach your network defenses, to internal threats like rogue employees or insecure configurations. These threats, if left undetected, can lead to serious consequences such as data loss, system downtime, and reputational damage. However, detecting these threats can be challenging, due to the sheer volume and complexity of data generated by today’s IT systems.

Often it can be a challenge to collect data into a monitoring environment that does not natively support that data source. Bindplane can help solve this problem. As the Bindplane Agent is based on OpenTelemetry (and is also as freeform as possible), one can bring in data from disparate sources that are not easily supported by the Splunk Universal Forwarder.

As a leader in Security Analytics, we at Elastic are often asked for our recommendations for architectures for long-term data analysis. And more often than not, the concept of Limitless Data is a novel idea. Other security analytics vendors, struggling to support long-term data retention and analysis, are perpetuating a myth that organizations have no option but to deploy a slow and unwieldy data lake (or swamp) to store data for long periods of time. Let’s bust this myth.