When managing queues and services in streaming data pipelines that use technologies like Kafka and RabbitMQ, SREs and application developers often struggle to determine if these pipelines are performing as expected. Visibility into the performance of a streaming data pipeline, after all, requires visibility into every component of that pipeline.

As the complexity of modern software development lifecycles increase, it’s important to have a comprehensive monitoring solution for your continuous integration (CI) pipelines so that you can quickly pinpoint and triage issues, especially when you have a large number of pipelines running.

The pressure to release application features faster to meet the demands of customers presents a number of challenges, including unforeseen deployment delays, custom feature sets, and complex rollbacks when errors occur. To overcome these challenges, developers can use Flagsmith, an open source feature flagging and remote configuration service that allows developers to easily roll out and test new features for a specific subset of users.

Azure App Service is a fully managed platform-as-a-service (PaaS) solution for deploying web applications, event-driven functions, RESTful APIs, and more. Azure App Service enables developers to quickly build and release services that scale dynamically—without worrying about provisioning and maintaining infrastructure. Last year, we released the Datadog extension for Azure App Service for deep visibility into your Windows.NET applications.

As organizations evolve and their stacks become more complex, they need increasingly robust visibility into their cloud-based infrastructure, services, and applications. Meanwhile, teams within these organizations tend to become more specialized and siloed. As they multiply across offices and time zones, these teams must be enabled to collaborate flexibly without muddling their distinct day-to-day priorities.

With the growing adoption of automated deployment tools, many organizations are releasing code more frequently. As releases increase, it’s important to ensure that you don’t accidentally introduce faulty deployments, which can have wide-ranging impacts on your infrastructure, application, and end-user experience, and can potentially lead to costly rollbacks.

In any type of organization and at any scale, logs are essential to a comprehensive monitoring stack. They provide granular, point-in-time insights into the health, security, and performance of your whole environment, making them critical for key workflows such as incident response, security investigations, auditing, and performance analysis. Many organizations generate millions (or even billions) of log events across their tech stack every day.

Complex systems require many different monitors to assess the health of their infrastructure and applications, creating a wealth of alerts that can be hard to track. Due to a lack of effective triage processes, many organizations page engineers for every alert that comes in, making it difficult to separate false positives from issues that actually require immediate attention.

Logs provide valuable information for troubleshooting application performance issues. But as your application scales and generates more logs, sifting through them becomes more difficult. Your logs may not provide enough context or human-readable data for understanding and resolving an issue, or you may need more information to help you interpret the IDs or error codes that application services log by default.

When your organization relies on hundreds or thousands of hosts, it can be difficult to ensure that each is equipped with the proper tools and configurations. Configuration management tools like Ansible are designed to help you automatically deploy, manage, and configure hosts across your on-prem and cloud infrastructure. In this post, we’ll show you how to use Ansible to automate the installation of the Datadog Agent on a dynamic inventory of Windows hosts.

AWS Lambda Function URLs make it even easier to create AWS Lambda functions that can be accessed and triggered by using HTTP/S requests, which is key for building serverless applications that are connected to and invoked from the web. Now you can generate a URL in one click that points to a specified Lambda function. Then, any HTTP/S request that a Function URL receives will trigger the Lambda function it’s assigned to.