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Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - February 2026 Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
DASH 2024: Guide to Datadog's newest announcements for teams
2024-06-26 · via Datadog | The Monitor blog

Datadog breaks down the silos that separate teams by giving everyone a unified view into the health, performance, and security of their organization’s environment. At this year’s DASH, we announced new features and capabilities to help organizations expertly manage access to data, giving all stakeholders the visibility they need while maintaining compliance. Datadog now collects and visualizes DORA metrics so you can track your software delivery velocity and stability. Teams can livestream and record terminal sessions with CoTerm for transparency into incident investigations. And Datadog Sheets lets teams analyze and share data using native spreadsheet functionality.

In this post, see these and other offerings to help you break down silos and give all teams full visibility into the data they need. Then, check out our keynote roundup for other announcements, including Datadog On-Call and much more.

Track service reliability and delivery

Improve the velocity and stability of software delivery with DORA metrics

Improve the velocity and stability of software delivery for your team or organization by tracking DORA metrics with Datadog. DORA metrics are four key engineering metrics—deployment frequency, lead time for changes, change failure rate, and time to restore service—that have emerged from more than 10 years of research by Google as key indicators of software delivery and operations performance.

Datadog now provides automated collection of DORA metrics via integrations with existing telemetry on the platform. See our DORA metrics documentation for more information and setup instructions.

Collect DORA metrics with Datadog.

Get a holistic view of your organization’s reliability with Datadog’s out-of-the-box SLO dashboard

Datadog’s new, out-of-the-box SLO dashboard supports aggregated views of SLOs to help engineering leadership understand their organization’s reliability at a glance. Customers can customize the dashboard to be based on groupings such as service, team, user journey, or any other tag that has been added to SLOs. A summary score, based on the remaining error budget of the underlying SLOs, makes it easy to understand SLO performance across different groups and identify areas of improvement. Once customers review their overall SLO performance, they can drill into more levels of detail to understand why certain groups are performing better or worse than others.

See our documentation for more details.

Give teams access to the data they need

Stream, record, and search terminal sessions with Datadog CoTerm

When investigating and resolving incidents, terminals remain an important tool alongside IDEs and web UIs like Datadog and the AWS Console. But communicating activity and progress that took place in a terminal session and storing that data for future reference and training is a manual, cumbersome process. Datadog CoTerm enables you to livestream, record, and log the contents of your terminal sessions, which you can then easily replay in a video-like player and query using full-text search. With CoTerm, you can seamlessly communicate progress around incidents, improve compliance when making changes to production systems, and review full attack lifecycles rather than just isolated evidence. See our dedicated blog post for more details.

Stream, record, and search terminal sessions with Datadog CoTerm

Query data from all of your Datadog orgs with cross-organization visibility

Some companies create and maintain multiple Datadog organizations within the same account to isolate data for a range of compliance, legal, and security reasons. But isolating critical observability data limits opportunities for collaboration, making it harder to achieve your observability goals. Now, cross-organization visibility enables customers to query metrics data between Datadog organizations within the same account and get insights from multiple organizations in one place. Cross-organization visibility is currently in Preview. Reach out to your Technical Account Manager or Customer Success Manager for more information.

Query data from across your Datadog orgs within the same account

Build custom self-serve remediation tools with App Builder

When platforms and monitoring data maintained by DevOps teams are siloed so that customer support, product, and other teams can’t access them, bottlenecks arise that can slow down critical response times. Datadog App Builder is a low-code platform for creating applications that you can use directly within Datadog, including within dashboards. Apps empower users across your organization to accelerate remediation, self-serve for common operations tasks, and glean unique monitoring insights from Datadog’s monitoring data. Apps can handle complex tasks with little end-user effort—summarizing pull requests, scaling container clusters, running serverless functions, and more. For more information, see our blog post.

App Builder DynamoDB Console app.

Analyze your data in a native spreadsheet interface with Datadog Sheets

For many teams, spreadsheets remain a powerful and necessary way to organize and analyze data. But downloading data from Datadog to a .CSV file means building ad-hoc analysis in Excel or Google Sheets that can become stale immediately. With Datadog Sheets, you can now manipulate, transform, and analyze logs, real user monitoring data, and metrics data in a familiar spreadsheet interface. For example, when investigating errors, you can open a subset of logs errors in Sheets, run an IF function to map each error to a high level category, and analyze the distribution of errors per category using a pivot table. You can also join real user monitoring data with external user data using our familiar lookup feature in order to enrich your usage analysis.

Join our Preview by filling out this form.

Analyze monitoring telemetry through spreadsheet functionality with Datadog Sheets

Manage and secure sensitive data access

Protect sensitive information with Data Access Controls

Enterprise customers need a way to make sensitive data within Datadog—PII, financial information, vulnerabilities, etc.—private to meet their security, regulatory, and internal compliance requirements. Datadog Data Access Controls provides customers the ability to use tags to restrict which data their users can query to protect this data and enable them to use it within Datadog to enrich their monitoring and security work with real business context. Data Access Controls are currently in Preview, with initial support for RUM and custom metrics data types. Reach out to your Technical Account Manager or Customer Success Manager for more information.

Protect sensitive information with Data Access Controls.

Manage your Datadog email notification recipients with the Domain Allowlist

The Domain Allowlist enables you to control which domains Datadog email notifications can be sent to. Customers can now secure email notifications from Datadog monitors within the boundary of your organization and other trusted domains, thereby safeguarding sensitive data and improving the efficiency of communications.

Improve your organization’s security and governance today with the Domain Allowlist by signing up for the Preview form here and referencing the docs.

Secure email notifications to trusted domains
Secure email notifications to trusted domains