Operations | Monitoring | ITSM | DevOps | Cloud

Google Cloud Next is the biggest event of the year for the Google Cloud community, showcasing the latest and greatest offerings from Google Cloud and hundreds of its partners. As a long-time Google Cloud partner and recipient of three Google Cloud Partner of the Year awards in 2025, Datadog was there in full force, delivering several speaking sessions and running a booth on the expo floor where we met with thousands of attendees. In case you missed it, don’t worry.

Datadog dashboards provide a unified view of your applications, infrastructure, logs, and other observability data—making it easy to monitor health, investigate issues, and share insights across teams. While native Datadog widgets support a broad range of visualization types, some use cases call for more customized representations, particularly when you’re working with unconventional data formats, external sources, or specific transformations.

Hallucinations occur when a large language model (LLM) confidently generates information that is false or unsupported. These responses can spread misinformation that jeopardizes safety, causes reputational damage, and erodes user trust. Augmented generation techniques, such as retrieval-augmented generation (RAG), aim to reduce hallucinations by providing LLMs with relevant context from verified sources and prompting the LLMs to cite these sources in their responses.

To gain adequate visibility into your distributed applications, you need to observe those applications at different levels of granularity. This means that you need to be able to query collected telemetry data both at the level of the whole application and at the level of selected components. Thanks to the power of Datadog tagging, you can already do this by aggregating your metrics within any scope of your choosing.

Security, operations, and development teams rely more and more on the ability to efficiently query logs. As these teams monitor the health, performance, and usage of their systems and investigate incidents, delving into log data can often be a matter of urgency.

As your organization scales its Datadog footprint, you want to understand what’s driving cost changes and promote cost awareness. But to take meaningful action, you need more than a monthly bill—you need real-time, contextualized cost data tied to services and teams. Without this visibility, it’s hard to assign ownership, prevent cost overruns, or identify which changes are affecting spend.

Before we started using Datadog Real User Monitoring (RUM), we relied on frontend logging to gather data about the user experience. Logs gave us some helpful information about exceptions and errors but didn't provide any insight into issues directly related to the user’s perspective.

Businesses deploying LLM workloads increasingly rely on LLM proxies (also known as LLM gateways) to simplify model integration and governance. Proxies provide a centralized interface across LLM providers, govern model access and usage, and apply compliance safeguards for smoother operations and reduced complexity—making LLM usage more consistent and scalable.

Finding the right integrations, libraries, and open source tooling to extend a product has long been a challenge for developers. While Datadog has a vast offering of monitoring and observability solutions, many teams need to customize their setup in some way—whether by extending the Datadog Agent, integrating with third-party services, or using SDKs to interact with the Datadog API.

Mobile developers are increasingly adopting Kotlin Multiplatform to share business logic across iOS and Android. While Kotlin Multiplatform reduces duplication of code-writing efforts, it also introduces blind spots. Developers often lack real-time visibility into how shared code performs across platforms, making it harder to troubleshoot issues and monitor user experience.

If you’re a Datadog customer, you’re likely using our platform to gain visibility into your infrastructure and applications and to troubleshoot using logs, metrics, and traces when issues arise. To support these efforts, you’ll want access to the most granular telemetry signals and intuitive workflows that streamline your investigation.

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.