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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
Monitor your Azure Event Hubs with Datadog
2017-01-05 · via Datadog | The Monitor blog

Azure Event Hubs is a service for building real-time data pipelines in the Microsoft Azure cloud. Data is sent to an Event Hub in messages called “events”, each of which represents a discrete occurrence or measurement such as a user’s in-app action or a device reading. The Event Hub then stores the events in sequence for a configurable retention time, allowing “consumer” applications to stream or replay data from the Event Hub as needed. The service has many similarities to Kafka and the AWS Kinesis service offered in the Amazon cloud.

Whatever infrastructure you use, we aim to bring you full observability. So we’re pleased to announce that you can now monitor your Event Hubs alongside your Azure VMs, Azure SQL Databases, Azure App Service applications, and more.

Out-of-the-box Event Hubs dashboard in Datadog
Azure Event Hubs monitoring dashboard in Datadog
Out-of-the-box Event Hubs dashboard in Datadog

Keep your hubs humming

Event Hubs are designed for high throughput and low latency, but their capacity is constrained by the number of throughput units provisioned for a given Event Hub namespace. That’s why it’s important to monitor all your Event Hubs, both to confirm that they are performing as expected, and to ensure that you do not saturate your provisioned capacity.

Every throughput unit allows you 1,000 inbound messages, or 1 megabyte of incoming data, per second. If you exceed your provisioned capacity, incoming events will be throttled, events will be dropped, and an exception will be returned to the event publisher.

For most applications, you want to know if you are trending toward dropping data before it happens. Once you turn on the Datadog integration with Azure, you’ll be able to immediately monitor your consumed capacity and trigger an alert if you’re in danger of saturation.

Outbound data from Event Hubs is limited to 2 megabytes per second per throughput unit. And although outgoing messages are not subject to throttling exceptions, monitoring your outbound traffic can help you identify resource limitations that may be slowing down your consumer applications.

See your Event Hubs in context

By connecting Datadog to Azure and enabling the integration with Event Hubs, you’ll immediately have access to a full suite of metrics from Event Hubs and other Azure services. You can track Event Hub throughput, as described above, and set alerts on internal server errors and other failures that can cause problems for your downstream applications.

To supplement your standard Azure metrics, you can choose from Datadog’s more than 1,000 built-in integrations with infrastructure technologies, including IIS, SQL Server, and other Microsoft products. Datadog also provides full support for custom metrics in multiple programming languages and frameworks, including C#.

Full Azure observability

Once metrics from your Event Hubs are flowing into Datadog, you have all of Datadog’s advanced monitoring functionality at your fingertips. From a flexible graphing interface and drag-and-drop dashboards to algorithmic alerting on outlier detection and anomaly detection, Datadog has the tools you need to monitor modern infrastructure.

If you don’t have a Datadog account and are ready to bring observability to your Azure infrastructure, sign up for a free trial account here.