Operations | Monitoring | ITSM | DevOps | Cloud

When your cloud environment spans multiple cloud service providers (CSPs) and SaaS providers, it can be challenging to collect cost and usage data in a way that gives you complete visibility. Each provider formats its data according to a unique billing model, and these inconsistencies can leave you with fragmented information about your total cloud spend.

On the March episode of This Month in Datadog, Jeremy Garcia (VP of Technical Community and Open Source) covers Attacker Clustering, Auto Test Retries, and new Observability Pipelines features, including keyword dictionaries and several integrations. Later in the episode, Jinwu Liu (Product Manager) spotlights Reference Tables, which is now generally available, and Yash Kumar (Product Lead, Cloud SIEM) shows how these tables can be used to add context to detection rules in Cloud SIEM.

Azure Service Bus is a fully managed enterprise message broker that enables asynchronous messaging between distributed applications. It is designed to decouple application components, allowing them to communicate reliably, securely, and at scale. With Datadog’s Azure Service Bus integration, you can.

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.