Operations | Monitoring | ITSM | DevOps | Cloud

As your applications scale and generate more telemetry, it becomes increasingly difficult to sift through the data and analyze it against cost, business functions, and security measures. Logs, events, and other telemetry on their own may not include enough meaningful context or readable details, leading to slower troubleshooting, inefficient business processes, and higher costs.

The Datadog Action Catalog provides more than 1,400 actions to help you accelerate remediation across your infrastructure directly within Datadog. With actions, you can use Workflow Automation to configure workflows that automatically address issues as they happen and build custom apps in App Builder that empower anyone in your organization to act when incidents occur.

A well-structured on-call rotation helps you ensure the reliability of your services and meet your customers’ expectations by designating staff to respond to emerging issues. But the pressures of on-call work—such as long shifts, overnight hours, and dynamic situations—can compromise the well-being of your team members. This makes it harder for them to maximize service uptime during their on-call shifts and can limit the velocity of the feature work they do outside of their on-call duty.

Empowered engineers and effective tools are the foundation of incident management, and having a solid on-call process can help facilitate both. In practice, however, many paging approaches have the opposite effect, often overwhelming responders and increasing burnout. To create an effective paging strategy, organizations should focus responder attention on the most important issues and help facilitate a sense of ownership over them.

State, local, and education (SLED) organizations need their logs to provide clear, structured insights into system performance, user behavior, and security risks. But often, the picture becomes scattered and chaotic instead, with critical log data buried in noise and gaps that make logs difficult to interpret.

The adoption of Datadog in large enterprises typically goes beyond integrating metrics, traces, and logs to unify observability. These enterprises must implement and use Datadog in a compliant and standard way across divisions, teams, and projects to enhance data security, comply with regulations, manage costs, and increase operational efficiency.

AI-powered coding tools are becoming more commonplace within developer workflows. GitHub Copilot is a popular AI coding assistant that can be integrated directly into IDEs or as a standalone chat interface. This tool helps you write code faster and with less effort by auto-completing code in real time, generating blocks of code from natural language prompts, and answering your questions to help you get over coding hurdles and roadblocks.

Java remains one of the most widely used programming languages today, especially in enterprise backend systems—and for many good reasons. With each new release, Java’s robust runtime offers additional improvements in performance, security, scalability, and developer productivity. The portability of its code has proven increasingly relevant and useful as the industry embraces ARM64, making Java one of the go-to languages for modern workloads.

Web applications generate a wealth of performance data, but it’s challenging to know exactly which metrics are the most useful for monitoring your user experience. Focusing on irrelevant metrics wastes time and resources—but if you pare down the data you’re observing too much, you may miss critical insights.

As mobile gaming evolves, players have higher expectations for seamless experiences, real-time interactions, and cross-platform accessibility. Whether you’re developing games for iOS, Android, or another mobile operating system, maintaining and optimizing the performance of your game is critical for player retention. For instance, if a mobile game becomes laggy or begins to drop frames during gameplay, players will grow frustrated and abandon the game altogether.