The company needed a practical way to bring CDN observability into Datadog, where its broader monitoring strategy already existed. The solution needed to support high traffic periods, provide clear visibility across regions and storefronts, and help teams catch problems before they affected revenue. It also had to work within existing operational controls without adding unnecessary complexity.

In this case study, we outline how we helped a retail organization establish end-to-end CDN observability in Datadog using Akamai delivery logs, security events, Synthetic Monitoring, and Real User Monitoring. The result was faster issue detection, better performance oversight, improved cache and origin visibility, and a more scalable operating model for digital commerce.

About the Customer

The customer is a retail enterprise with a large digital footprint across ecommerce, customer support, and mobile experiences. Its online platform supports high volumes of product browsing, seasonal campaigns, loyalty traffic, and transaction flows that are directly tied to revenue and customer satisfaction.

Because the business operates in a highly competitive environment, digital performance is never treated as a back-end concern. Slow product pages, intermittent failures, or unreliable checkout behavior can quickly affect conversion, increase abandonment, and put pressure on customer support teams. The company already relied on Datadog for broad observability and used Akamai to support content delivery and edge security, but CDN-specific visibility had not yet been operationalized in a consistent way.

Customer Challenge

As traffic volumes and digital complexity increased, the customer’s existing monitoring approach became harder to scale and less suited for proactive operations.

Key challenges included:

Limited visibility into CDN performance and behavior

The team could see parts of the picture, but not the full delivery path. It was hard to pinpoint whether issues were driven by the edge, origin, content type, geography, or user segment.

No single source of truth for delivery, experience, and security signals
Request logs, error trends, cache behavior, security events, and customer experience data were rarely looked at together. As a result, investigations often required multiple teams and multiple tools.

High sensitivity to traffic spikes and campaign periods
Promotions, seasonal peaks, and product launches created large bursts of traffic. During these periods, the customer needed earlier visibility into latency increases, error spikes, cache inefficiencies, and origin stress.

Limited ability to distinguish infrastructure issues from customer experience issues
The operations team needed to know not just whether the CDN was responding, but whether customers were actually experiencing slow page loads, broken assets, or degraded journeys.

Need for stronger operational control without added complexity
The customer wanted to extend observability using its existing Datadog ecosystem, rather than build a separate monitoring silo for CDN data.

These gaps made it harder to detect emerging problems quickly, prioritize the right response, and protect the performance of revenue-generating digital journeys.

Proposed Solution

We came into the engagement with a clear objective: bring CDN observability into the customer’s existing Datadog environment in a way that was operationally simple, scalable, and useful for both engineering and business teams.

The solution combined several complementary telemetry sources to create a more complete operational view:

• Akamai DataStream 2 logs to capture CDN request, response, cache, geography, and edge performance data
• Akamai security event data to provide visibility into edge security activity, suspicious traffic, and rule-driven detections
• Datadog dashboards and log-based metrics to turn raw delivery data into usable operational views and monitors
• Datadog Synthetic Monitoring to validate availability, DNS resolution, TLS health, and critical user journeys from multiple regions
• Datadog RUM to understand the actual customer experience across product pages, search, cart, and checkout

The aim was not only to collect more telemetry, but to build a practical observability model for retail operations. That meant organizing the data around the questions the customer needed to answer every day: Is the site fast? Are users seeing failures? Is the cache working efficiently? Are campaigns creating unexpected pressure? Is suspicious traffic affecting digital performance?

Unified CDN Visibility

The first priority was to create a centralized view of CDN activity inside Datadog. Akamai delivery logs were structured into meaningful dimensions such as hostname, path, geography, status code, content type, cache status, and edge location. This gave the customer a consistent way to understand traffic and delivery patterns across storefronts, customer flows, and regions.

Instead of relying on fragmented log views, teams could now see request volumes, response codes, latency distribution, large payloads, and path-level behavior in one place. This improved day-to-day situational awareness and cut down the time needed to determine whether an issue was localized or systemic.

For a retail environment, that level of visibility is particularly valuable during campaign periods. The team could quickly tell whether a spike was concentrated on promotional landing pages, product detail pages, account journeys, or checkout endpoints, and could assess whether the CDN was handling the increased demand as expected.

Performance Monitoring and Experience Assurance

We designed the observability model to connect delivery performance with actual customer experience. On the CDN side, the customer gained visibility into response times, regional variations, status trends, and the performance profile of high traffic pages. This helped the team spot slowdowns early and understand whether they were linked to specific paths, countries, or edge patterns.

To complement this, we introduced Synthetic Monitoring for key digital journeys. Synthetic tests covered homepage availability, product navigation, search, cart access, and checkout health from strategically selected locations. This gave the customer an outside-in view of whether the platform remained reachable and responsive under real-world conditions.

We also aligned CDN observability with RUM data so that the team could validate whether a backend or edge issue was visible to real users. This was especially important for troubleshooting scenarios where logs showed healthy request handling, but customers were still experiencing poor page performance due to asset delays, browser-side bottlenecks, or region-specific issues.

By combining delivery telemetry, synthetic validation, and user experience data, the customer moved from reactive monitoring to a more complete experience assurance model.

Availability and Incident Detection

A major part of the value came from improving how the customer detected and responded to incidents. We created monitors for traffic drops, rising error rates, unusual latency changes, and geographic anomalies. These monitors gave the operations teams earlier warning when a service degradation was emerging, rather than after it  had already been escalated by customers or support teams.

The monitoring design was especially useful for retail use cases where timing matters. Product launch days, campaign events, and flash sales can create very narrow windows where degraded performance has an immediate business impact. With better alerting around 4xx and 5xx trends, path-specific error concentrations, and region-level response changes, teams could identify issues faster and focus on the right area of investigation.

Because the data was all available in Datadog, investigation workflows also became more efficient. Teams could move from a CDN latency spike to the affected paths, from there to real user experience signals, and then to related application behavior, without switching contexts across disconnected tools.

Cache Efficiency and Origin Protection

For a retail platform, CDN observability is not only about speed. It is also about controlling how effectively the CDN protects origin systems during periods of heavy demand. We used delivery telemetry to make cache behavior visible in a way that was actionable for operations and engineering teams.

This included tracking cache outcomes, identifying uncached high traffic paths, comparing latency by cache behavior, and highlighting patterns that could increase origin dependency. The result was a clearer view of whether static and semi-static content was being delivered efficiently and whether avoidable cache misses were contributing to unnecessary origin load.

This was particularly relevant for product images, campaign content, informational pages, and high demand browse traffic. With cache efficiency now measurable, the customer gained a stronger foundation for performance tuning, release validation, and origin protection during peak periods.

Security and Traffic Risk Monitoring

Retail platforms face a constant mix of legitimate customer traffic and potentially harmful automated activity. We extended the observability model to include Akamai security events so the customer could monitor suspicious behavior alongside delivery performance.

This allowed the team to see spikes in rule-triggered events, suspicious request patterns, unusual client concentration, and traffic segments that warranted further investigation. Bringing these signals into Datadog helped security and operations teams work from the same context, which improved both awareness and response coordination.

For the customer, this was valuable not only from a security perspective but also from a performance and availability standpoint. Unwanted traffic can distort traffic baselines, increase load on important journeys, and complicate incident triage. With better visibility into suspicious activity, the team could separate customer-driven demand from risk-driven noise more effectively.

Business and Operational Insights

One of the strongest outcomes of the engagement was that CDN observability became useful beyond infrastructure operations. The data made it easier to understand which markets were driving traffic, which routes carried the highest demand, and how digital performance behaved during business events.

This gave the customer a better operational lens on digital commerce. Teams could compare regional traffic patterns, evaluate the impact of promotions, assess which pages were most performance-sensitive, and prioritize optimization efforts with stronger confidence. Instead of viewing the CDN purely as a technical layer, the organization gained a more practical understanding of its role in customer experience and digital revenue protection.

Outcomes and Success Metrics

The CDN observability solution delivered clear operational and business benefits for the team.

Centralized Visibility

• Established a single Datadog-based view of CDN traffic, delivery performance, cache behavior, and edge security activity
• Standardized dashboards and monitors across key digital journeys and storefront environments
• Improved visibility across regions, paths, and high value retail transactions

Faster Incident Detection and Resolution

• Reduced time to identify latency spikes, path-specific failures, and regional delivery issues
• Improved incident triage by correlating CDN telemetry with synthetic tests and real user experience data
• Enabled earlier response to traffic anomalies during campaign and peak demand periods

Stronger Customer Experience Oversight

• Provided a clearer view of how CDN behavior affected homepage, browse, search, cart, and checkout performance
• Helped teams distinguish platform health from actual customer experience impact
• Supported more proactive performance management for critical journeys

Better Cache and Origin Efficiency

• Increased visibility into cache effectiveness and origin dependency
• Helped identify optimization opportunities for high traffic content and delivery patterns
• Strengthened origin protection during peak periods by making cache behavior operationally visible

Improved Security Context

• Consolidated edge security signals with delivery telemetry inside Datadog
• Improved the ability to detect suspicious traffic patterns and investigate their operational impact
• Enabled more coordinated response across operations and security functions

Scalable Operations

• Extended observability using the customer’s existing Datadog strategy
• Reduced reliance on fragmented tooling and manual investigation workflows
• Created a monitoring model that can scale with future digital growth, new storefront requirements, and evolving customer journeys

About Crest Data

By bringing CDN observability into Datadog, we helped the customer move from fragmented monitoring to a more complete and operationally useful model for digital performance, connecting delivery telemetry, security events, synthetic validation, and real user experience into a single view that enabled faster response and better decision-making. For a retail organization, this created value well beyond infrastructure monitoring, improving visibility into customer-impacting issues and strengthening resilience during peak traffic periods. 

As an Advanced Datadog Partner specializing in Cybersecurity and Observability, Crest Data brings the expertise to help enterprises build scalable, insight-led observability solutions that protect digital experience at scale.