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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 - 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Monitor Datazoom telemetry with Datadog
Jordan Obey · 2022-06-03 · via Datadog | The Monitor blog

Modern video streaming workflows are composed of many different services, including encoders, origins, ad servers, content delivery networks (CDNs), and more. This wide range of options enables organizations to choose the tools that best fit their needs, but it also introduces considerable observability challenges. For instance, you may have limited access to the log data from each layer of your video workflow, and the data you can access likely isn’t standardized. This inconsistency makes it much harder to make correlations and identify the root cause of a streaming issue.

To overcome this challenge, Datazoom collects event-level, real-time data across different components of your streaming service—and standardizes it into easy-to-parse JSON. Our integration with Datazoom enables you to send all of your video workflow data to Datadog, where you can visualize, analyze, and alert on it in real time. In this post, we’ll look at how Datadog and Datazoom work together to provide visibility into your video player, CDN, and other components of your video streaming workflow.

Get a centralized view of all your video workflow data

Once you enable the integration, telemetry from Datazoom will become available in an out-of-the-box dashboard, providing a centralized view of the health and performance of your streaming service.

The Datazoom dashboard provides a centralized view of all your video workflow data.

The dashboard includes key Quality of Experience (QoE) metrics such as the average Time to First Frame (TTFF), which measures the elapsed time between when a user clicked play and when their video began. It also visualizes Rebuffer Ratios, which describe the portion of playback time a user spent waiting for a video to buffer. Monitoring and alerting on these QoE metrics can help you identify delays and interruptions in video playback that can reduce customer satisfaction and lead to churn.

The dashboard also comes with default template variables that enable you to dynamically filter your data. For instance, you can home in on specific browsers (with the browser_family variable) or playback sessions (with the content_session_id variable) to better understand the scope of an issue.

You can also customize your dashboard to track telemetry from other key components of your infrastructure. For instance, if you’re using Amazon DynamoDB to store video metadata, you can add widgets that visualize your table’s read and write latencies, as well as the number of throttled requests.

Correlate playback data with upstream service performance

While playback performance metrics can tell you that an issue is occuring, they often can’t tell you why it’s occurring. In order to identify the cause, you also need visibility into your video workflow’s upstream services. Datazoom collects telemetry from CDNs like Akamai, Cloudfront, Fastly, and Lumen, which allows you to easily correlate video player and CDN performance. This way, if you are notified of a sudden TTFF spike, you can pivot seamlessly to CDN metrics such as Cache Hit Ratio, which compares the number of content requests your CDN’s cache filled successfully with the total number of requests it received. If your cache hit rate is lower than 95 percent, you may need to adjust your cache control settings.

Datadog visualization tools allow you to easily correlate playback data with upstream service performance.

You can also monitor playback performance alongside other upstream services. For instance, Datadog integrates with Amazon Simple Storage Service (S3), which is often used to store and package videos. When cache misses occur, CDNs will request data from your origin directly, which can increase latency. In order to mitigate this issue, it’s important to ensure you’ve sufficiently optimized your buckets with techniques such as parallelization.

Dive deeper into issues with standardized video workflow logs

Datadog collects Datazoom logs, so you can get an in-depth look at events that occur at every point during your video streaming workflow. Datazoom logs are automatically enriched with tags, which provide contextual details about video workflow events that can help you investigate further. For example, if you’re notified of a low cache hit ratio, you can quickly filter your Datazoom logs by the associated CDN and region in which the issue occurred to accelerate your troubleshooting.

Use the log explorer to filter and investigate your Datazoom logs.

Start monitoring Datazoom video workflow data today

Datadog allows you to monitor Datazoom telemetry alongside more than 1,000 other services and technologies, providing you with an end-to-end view of your entire video workflow in a single, unified platform.

To learn more about how Datadog and Datazoom can help you deliver a high-quality user experience to your customers, check out our documentation. If you aren’t already using Datadog, get started today with a 14-day free trial.