Operations | Monitoring | ITSM | DevOps | Cloud

Delivering software quickly and reliably is the main focus of modern DevOps. But to improve your delivery performance, you need to understand it, and that starts with measurement. Teams primarily measure performance in this area by using DORA metrics—deployment frequency, change lead time, change failure rate, and time to restore service*. These metrics help teams understand trends in their software delivery practices in quantifiable terms that they can track and improve over time.

As your applications scale across cloud and SaaS providers, allocating costs and optimizing workloads become increasingly important—and challenging. Without access to cost data in their daily workflows, engineering teams can’t easily understand the cost of their resources and identify where they can reduce their spend. And while FinOps teams have access to cost data, they often review this information in silos.

Airflow is a popular open source platform that enables users to author, schedule, and monitor workflows programmatically. Airflow helps teams run complex pipelines that require task orchestration, dependency management, and efficient scheduling across many different tools. It’s particularly useful for creating data processing pipelines, orchestrating task-based workflows such as machine learning (ML) training, and running cloud services.

In Part 1 of this series, we looked at key Airflow metrics to monitor. Now, we’ll explore how you can collect those metrics, along with logs and traces, using Airflow’s native tooling. We’ll also look at a few key ways you can monitor this data from within the Airflow webserver interface.

In Part 1 of this series, we discussed key metrics for monitoring Airflow. In Part 2, we discussed strategies for collecting Airflow metrics, logs, and lineage. Finally, we will look at how you can use Datadog to monitor all this telemetry in a single consolidated view alongside telemetry from the rest of your infrastructure and services.

We are thrilled to announce that Datadog has been named a Leader in the Forrester Wave: AIOps Platforms, Q2 2025. We believe this placement reflects Datadog’s commitment to offering an AI-driven platform that enables customers to observe and secure systems, orient teams, and take action in one place. Datadog sits within your most critical workflows, processing trillions of telemetry data points every hour through your alerts, service maps, teams, on-call schedules, and more.

Risk assessment requires context. One of the primary challenges with protecting cloud environments is understanding how certain activity can lead to risk. Risky behavior can be categorized as any activity or action that increases the likelihood of an attack in your cloud environment. While certain activity may not be malicious on its own, it can expand an environment’s attack surface or indicate post-compromise behavior.

Modern code profilers gather performance data that is highly useful for developers, but the traditional presentation of that data can be challenging to interpret for engineers who are new to profiling. For the Continuous Profiler team at Datadog, our guiding mission is to make profiling a standard practice for all developers by flattening its learning curve and helping teams quickly gain insights into application performance.

Blind spots in frontend monitoring can occur when you’re managing complex modern applications. Browser and device variability, user journeys with intricate workflows and multiple touchpoints, and ephemeral frontend components can all create visibility gaps that make it difficult to identify, understand, and resolve the issues impacting user experience.

Engineering teams constantly balance the need for speed and standardization, but achieving both goals at the same time often feels impossible. Developers’ dependence on platform engineers for support with infrastructure and tooling can create bottlenecks for routine operational tasks such as provisioning environments, troubleshooting incidents, and managing deployments.