Stay on top of issues and easily forecast usage for Ably-powered realtime applications in your out-of-the-box Datadog dashboards. Cloud-based applications providing live user experiences at scale need to measure their realtime infrastructure’s state and health at all times. Imagine a customer support chat system supporting thousands of businesses or a live sport event delivering updates to millions of fans.

Increased costs and wasted resources are on the rise as software systems have moved from monolithic applications to distributed, service-oriented architectures. As a result, over the past few years, interest in observability has seen a marked rise. Observability, borrowed from its control theory context, has found a real sweet spot for organizations looking to answer the question “why,” that monitoring alone is unable to answer.

When monitoring cloud resources, there are several factors to consider.

When monitoring cloud resources, there are several factors to consider.

This article will teach you how to monitor Ruby errors without using a service like Honeybadger! Learn about Ruby's built-in errors, how to use log levels and tagging to customize the log output, display errors on the front-end, and more.

Access to data is critical for SaaS companies to understand the state of their applications, and how that state affects customer experience. However, most companies use multiple applications, all of which generate their own independent data. This leads to data silos, or a group of raw data that is accessible to one stakeholder or department and not another.

SmartBear is excited to announce the upcoming beta for real user monitoring, a new facet of BugSnag’s observability stack. BugSnag is a full-stack monitoring solution delivering real-time data to improve app performance and help you balance roadmap agility with application stability.

Companies who develop software products generate massive quantities of product performance and user engagement data that can be analyzed to support decision-making about everything from feature planning and UX design to sales, marketing, and customer support.

As fraud continues to grow in prevalence, SecOps teams are increasingly investing in fraud prevention capabilities to protect themselves and their customers. One approach that’s proved reliable is the use of log analytics and telemetry data for fraud prevention. By collecting and analyzing data from various sources, including server logs, network traffic, and user behavior, enterprise SecOps teams can identify patterns and anomalies in real time that may indicate fraudulent activity.