Operations | Monitoring | ITSM | DevOps | Cloud

Ship features faster and safer with Datadog Feature Flags

Releasing new features is one of the highest-stakes moments in the software delivery life cycle. Even with CI/CD pipelines in place, plenty of things can still go wrong when a feature goes live for actual users. Most feature flagging tools operate in isolation from important observability tooling, forcing engineers to monitor changes across multiple disconnected systems to fully understand their impact. This slows down development and increases the chance of missing critical issues.

Datadog Feature Flags, track Claude costs, migrate historical logs, and more | This Month in Datadog

See how you can reduce risk during feature rollouts in September’s This Month in Datadog. This episode, we spotlight Datadog Feature Flags, which combines advanced targeting with built-in observability, and guardrails to make rollouts safer and more controlled. Plus, we cover: This Month in Datadog brings you the latest updates on our newest product features, announcements, resources, and events.

From Logs to Insights: Accelerate Customer-Impact Analysis with Datadog Sheets

Datadog Sheets helps you move from log exploration to actionable insights quickly and with no code required. In this demo, see how to enrich logs with Salesforce data, build pivot tables, uncover customer impact trends, and build shareable reporting, all within Datadog.

Model your architecture with custom entities in the Datadog Software Catalog

Every software organization has its own unique architecture and workflows. Beyond services and APIs, teams rely on internal libraries, CI/CD jobs, data pipelines, AI agents, and more to keep systems running smoothly. But as architectures grow more complex and interconnected, it can become difficult to keep track of all the structural dependencies and interactions in one place.

Monitor your data pipelines with Airflow lineage

In complex data pipelines with dozens of jobs and intermediary datasets, it can be difficult to effectively monitor how data travels and changes through various steps. When tracking issues in these pipelines, you need visibility into upstream components where the root cause may originate from, as well as downstream datasets and consumers of data that may be experiencing further impacts.

Proactively monitor Kerberos-authenticated web apps and APIs with Datadog Synthetics

When employee authentication fails or becomes unreliable, users can lose access to the critical systems they need. Authentication enables access to internal tools like HR applications, finance portals, and internal dashboards, so even short outages can interrupt day-to-day work, while persistent issues increase the risk of broader operational disruption.

Datadog in the Era of AI

AI is changing everything. At Datadog, our approach is two-fold: empower you with complete observability across your entire stack, including AI as you incorporate it, and harness emergent technologies to make Datadog even more powerful. Join VP of Product Michael Whetten to see how Datadog is accomplishing these two approaches. He'll share the latest feature updates and new products designed to help you thrive in an AI-powered world. Plus, get a look at our long-term vision for the future of AI and its impact on your work.

Track the performance of your HPC workloads with Datadog's AWS PCS integration

AWS Parallel Computing Service (AWS PCS) is a managed service that helps users run and scale their high performance computing (HPC) workloads. AWS PCS uses Slurm, an open source workload manager, for scheduling and orchestrating simulations, which enables users to build their scientific and engineering models in a familiar HPC environment.

Monitor Windows Certificate Store with Datadog

The Windows Certificate Store is a critical component of any modern Windows environment. Certificates enable TLS encryption for Internet Information Services (IIS)-hosted applications, support certificate-based authentication in Active Directory, and help validate the identity of trusted Windows services. But if a certificate in your store expires, is revoked, or is part of a broken certificate chain, you risk instability and security gaps in your Windows environment.

Visually identify observability gaps with Cloudcraft in Datadog

Modern cloud environments are highly complex and dynamic, with critical services relying on large numbers of ephemeral resources. Ensuring observability coverage across this landscape is essential for troubleshooting, maintaining reliability, optimizing performance, and enforcing security standards. But as environments grow more elaborate and their ownership more dispersed, tracking observability coverage becomes increasingly challenging.

A practical guide to error handling in Go

When you first start coding in Go, you quickly learn how error handling in the language differs from error handling in languages such as Java, Python, JavaScript, or Ruby. In those languages, throwing an exception automatically generates a stack trace. Go, by contrast, provides no built-in error tracing to reveal an error’s origin.

Understanding dbt: basics and best practices

Data Build Tool (dbt) is an open source analytics engineering framework that enables teams to transform raw data that has been loaded into a warehouse like Snowflake, BigQuery, Redshift, or Databricks using SQL-based workflows. dbt is available in two main forms: dbt Core, the free and open source CLI tool, and dbt Cloud, a managed platform that adds scheduling, UI support, collaboration tools, and native integrations.

Visually identify and prioritize security risks using Cloudcraft

As cloud infrastructure becomes more dynamic and distributed, DevOps and security teams need to quickly detect risks and understand their context: where those risks live, how critical they are, and how to respond effectively. By surfacing misconfigurations, vulnerabilities, sensitive data risks, and identity threats directly on a real-time diagram of your infrastructure, Cloudcraft helps teams identify, prioritize, and remediate security issues at scale.

This Month in Datadog - August 2025

In the August episode of This Month in Datadog, Jeremy shares how you can make more informed cloud cost decisions, gain insights into your LiteLLM-powered applications, and secure Kubernetes infrastructure with Datadog Workload Protection. Later in the episode, Danny puts the spotlight on Datadog Kubernetes Autoscaling, which helps you deliver cost savings without sacrificing performance.

Cost Controls and so Much More: Issue Detection Through Usage Analysis

Keeping tabs on cloud spending across multiple organizations and vendors, including Datadog, can be tough and costly. If you're not tracking expenses, you're also missing other critical insights. The Flight Centre Travel Group (FCTG) faced this when moving to Datadog, needing to monitor costs across numerous organizations and over 180 Azure subscriptions. After a rapid migration, new cost reports quickly revealed more than just financial benefits. Unusual spending patterns often highlighted incidents, bugs, or security issues, offering early warnings about internal system problems.

Bridging the Gap: Legacy Systems and Modern Observability

Technology moves quickly and while the spotlight has shifted to dynamic, cloud-based systems, many organizations have legacy applications and infrastructure that they must maintain. In this fireside chat, Datadog’s Matt Moore (Principal Observability Strategist) will host James Flores (Enterprise Systems Engineer) at Australian Community Media to discuss their journey of modernization and bridging legacy systems with the cloud using a bit of ingenuity and observability.