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By Datadog
Adding or renaming a product event has traditionally meant creating an engineering ticket. Datadog Product Analytics uses the same SDKs and configuration as Real User Monitoring (RUM), and those SDKs autocapture actions such as clicks and taps. But an automatically generated action name describes the element rather than the user intent behind it.
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By Datadog
Earlier this year, Datadog for Government achieved FedRAMP High certification, extending our GovCloud environment (US1-FED) to the federal government’s most sensitive civilian workloads. That certification now covers Datadog Incident Response, bringing paging, incident coordination, automation, and postmortem workflows into US1-FED. When a government system goes down, responders need to reach the right people, coordinate a fix, and keep stakeholders informed.
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By Datadog
AI-assisted development can increase the rate at which teams produce code, but teams only realize those velocity gains if CI can keep pace. More pull requests (PRs) mean more builds, tests, and pipeline executions. Slow jobs leave developers and coding agents waiting for feedback, flaky failures consume time in reruns and investigations, and unnecessary test execution increases runner demand as delivery volume grows.
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By Datadog
Earlier in this series, we covered key metrics for monitoring performance in Databricks and discussed Databricks’ native resources for accessing those metrics and other key observability data, such as logs and data lineage. In this post, we’ll cover using the Databricks integration to bring that data into Datadog and monitor your Databricks analytics and AI/ML workloads alongside the rest of your end-to-end data pipelines and distributed infrastructure. We’ll show you how to.
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By Datadog
In the first part of this series, we cataloged key metrics for Databricks data engineering, analytics, and Model Serving workloads. In this post, we’ll discuss how to collect those metrics and other telemetry data from Databricks and Apache Spark, which powers Databricks under the hood. We’ll cover collecting and querying telemetry data via system tables, as well as the other primary sources of visibility into.
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By Datadog
Databricks is a platform for analytics, AI, and data intelligence that has pioneered the lakehouse paradigm in modern data systems.
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By Datadog
You get a Slack message from the VP of Sales: They have asked an AI agent connected to Snowflake for the past quarter’s revenue and the numbers look wrong. First, you verify the agent’s query and, when that looks fine, check the pipelines that populate the underlying table. All jobs completed, the data is recently refreshed. Then it’s time to check the logs for errors. Nothing.
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By Datadog
Many revenue-critical interactions, such as ecommerce checkouts and customer portals, run on Shopify and Salesforce. But engineering teams have less control over the frontend runtime on these platforms, and this lack of control can make user monitoring difficult to implement and maintain. These monitoring limitations can leave gaps in visibility across important parts of the user journey.
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By Datadog
TypeSafe AI released Jev in September 2026 to do one thing: make decisions. Give it a state (a string or a JSON object) plus a set of typed questions, and it returns typed answers with probabilities. It never explains itself, and that constraint is the whole idea. Evaluation pipelines have spent the last two years asking text generators for yes/no verdicts, wrapping the reply in a JSON schema, and paying generation prices for what amounts to a single bit.
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By Datadog
Datadog Real User Monitoring (RUM) SDK settings live in your application code, so changing how the SDK collects RUM data has traditionally required shipping a new application version. These configuration changes can include adjusting sampling rates, enabling Session Replay, or changing which events the SDK collects. For mobile teams, this means that updates often sit in app store review for days or weeks before users start adopting the new version. Full user adoption can take weeks or months longer.
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By Datadog
Missing JMX metrics make it hard to know what’s happening in a Java application, especially when vague errors or configuration mismatches make the cause difficult to diagnose. In this video, you’ll see how to troubleshoot common JMX metric collection issues and isolate the cause in less time.
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By Datadog
See how Bits Chat turns a natural-language request into an automated incident response workflow. In this demo, Bits Chat builds a workflow that investigates a monitor alert, identifies whether a recent deployment caused the issue, rolls it back when appropriate, and sends a summary to Slack.
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By Datadog
Datadog Bits Agent Builder lets you build AI agents that use your observability data to automate operational tasks. In this walkthrough, see how to build an agent that analyzes monitor and alert activity, identifies patterns, and provides actionable recommendations to improve your monitoring strategy. With Bits Agent Builder, you can give agents access to Datadog data and tools, customize their instructions and models, and run them automatically to continuously analyze and act on your environment.
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By Datadog
Adding metadata to Software Catalog entities manually is a tedious process that doesn’t scale as your service count grows. This video shows you how to automate that work with Terraform so you can add shared metadata across existing Software Catalog entities at scale.
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By Datadog
In July’s This Month in Datadog, Jeremy is joined by Datadog product leaders for in-depth conversations about how Bits enables you to confidently evaluate and release features containing AI-generated code, and use natural language to ask, understand, and act across Datadog.
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By Datadog
AssemblyAI is a leading Voice AI platform that provides speech-to-text models and AI infrastructure developers use to build real-time voice applications. AssemblyAI uses Datadog to unify observability across its AI inference pipelines and multi-cloud GPU infrastructure, enabling the team to optimize performance and costs, accelerate model releases, and confidently deliver fast, reliable AI experiences at scale.
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By Datadog
At hyperscale, a regional cloud outage is not merely a technical disruption—for Samsung Account, which serves 2.1 billion users across three global regions, it is an immediate global service crisis. Fragmented, region-siloed monitoring creates blind spots that make early detection nearly impossible, leaving SRE teams perpetually reactive rather than predictive. The path to proactive reliability requires both a philosophical shift and a foundational change in how observability data is collected, unified, and reasoned over.
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By Datadog
Modernizing a legacy system serving 20 million devices without users noticing is like replacing a jet engine mid-flight. In this session, YoungJin Jung and Donggen Hong from LG U+ share their 18-month journey transforming a Telco-scale API Gateway from a rigid, proprietary solution into a high-performance, open-source architecture on AWS, and the operational challenges they solved along the way.
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By Datadog
Replace "AI shipped on hope" with an operating model that holds up once real users depend on it. AI quality is multi-dimensional, covering accuracy, tone, safety, and faithfulness to user data, and can't be debugged from outputs alone. Without visibility into what their AI actually did in production, teams miss regressions, reverse-engineer chains by hand, and watch a single bad answer erode trust built over hundreds of right ones.
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By Datadog
Every team is doing something with AI right now. What that something is, is an entirely different question. And whether that something is successful? Most teams are still figuring it out as they go.
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By Datadog
The elasticity and nearly infinite scalability of the cloud have transformed IT infrastructure. Modern infrastructure is now made up of constantly changing, often short-lived VMs or containers. This has elevated the need for new methods and new tools for monitoring. In this eBook, we outline an effective framework for monitoring modern infrastructure and applications, however large or dynamic they may be.
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By Datadog
As Docker adoption continues to rise, many organizations have turned to orchestration platforms like ECS and Kubernetes to manage large numbers of ephemeral containers. Thousands of companies use Datadog to monitor millions of containers, which enables us to identify trends in real-world orchestration usage. We're excited to share 8 key findings of our research.
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By Datadog
Where does Docker adoption currently stand and how has it changed? With thousands of companies using Datadog to track their infrastructure, we can see software trends emerging in real time. We're excited to share what we can see about true Docker adoption.
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By Datadog
Build an effective framework for monitoring AWS infrastructure and applications, however large or dynamic they may be. The elasticity and nearly infinite scalability of the AWS cloud have transformed IT infrastructure. Modern infrastructure is now made up of constantly changing, often short-lived components. This has elevated the need for new methods and new tools for monitoring.
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By Datadog
Like a car, Elasticsearch was designed to allow you to get up and running quickly, without having to understand all of its inner workings. However, it's only a matter of time before you run into engine trouble here or there. This guide explains how to address five common Elasticsearch challenges.
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By Datadog
Monitoring Kubernetes requires you to rethink your monitoring strategies, especially if you are used to monitoring traditional hosts such as VMs or physical machines. This guide prepares you to effectively approach Kubernetes monitoring in light of its significant operational differences.
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Datadog is the essential monitoring platform for cloud applications. We bring together data from servers, containers, databases, and third-party services to make your stack entirely observable. These capabilities help DevOps teams avoid downtime, resolve performance issues, and ensure customers are getting the best user experience.
See it all in one place:
- See across systems, apps, and services: With turn-key integrations, Datadog seamlessly aggregates metrics and events across the full devops stack.
- Get full visibility into modern applications: Monitor, troubleshoot, and optimize application performance.
- Analyze and explore log data in context: Quickly search, filter, and analyze your logs for troubleshooting and open-ended exploration of your data.
- Build real-time interactive dashboards: More than summary dashboards, Datadog offers all high-resolution metrics and events for manipulation and graphing.
- Get alerted on critical issues: Datadog notifies you of performance problems, whether they affect a single host or a massive cluster.
Modern monitoring & analytics. See inside any stack, any app, at any scale, anywhere.