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Do You Need Multi-CDN Monitoring? Here's What You Need to Consider - Part 2

Tony Falco
VP Marketing
Hydrolix

In Part 1 of this two-part series, I defined multi-CDN and explored how and why this approach is used by streaming services, e-commerce platforms, gaming companies and global enterprises for fast and reliable content delivery. Multi-CDN offers unmatched benefits when it comes to redundancy, availability, load balancing, scalability, cost management, and improved user experiences on a global scale. Now, in Part 2 of the series, I'll explore one of the biggest challenges of multi-CDN: observability.

Why Don't All Observability Platforms Monitor Multi-CDN?

If your log monitoring platform is capable of spanning multiple CDNs, you might be in good shape. Some do, but most don't. Those that do have differing levels of sophistication. Let's break down some of the details you'll want to examine.

1. Data Volume and Variety: Multi-CDN generates vast amounts of data from multiple sources. In fact, it's so much data that many multi-CDN users don't even try to analyze it because the storage costs alone can be eye watering. For observability platforms to provide the value you expect, they need to ingest, process, and store all this data in very near real time. With legacy systems, it's almost always resource-intensive and costly.

2. Real-Time Data Transformation: Effective multi-CDN monitoring requires the ability to transform and standardize data in real time, a capability that many platforms lack. Without this, data analysis becomes cumbersome and less accurate.

3. Unified View and Analysis: To effectively monitor multi-CDN performance, businesses need a unified view that combines data from all CDNs. This requires sophisticated data aggregation and query capabilities, which not all platforms can provide.

4. Considerations: As mentioned above, storing and processing multi-CDN data long-term is expensive. Many platforms limit data retention to 90 days due to cost constraints, which restricts the ability to perform historical analysis and trend forecasting. Unfortunately, historical data is where much of the value lies. The ideal log monitoring solution is one that keeps all data hot at minimal cost.

Few log monitoring platforms are capable of meeting all these standards. As a result, many companies end up throwing away their valuable log data, losing out on insights that quite literally describe the state of their business.

When Your Multi-CDN Has Access to All the Data

So, in the perfect world where all the elements above are working, what can having access to comprehensive multi-CDN log data unlock for your businesses?

First, there's real-time visibility and alerting. With data from multiple CDNs ingested into a unified view, companies can monitor performance in real time, detect issues early, and respond proactively to prevent disruptions — disruptions that frustrate customers and reduce revenue. Detailed IP address analytics can help identify and mitigate security threats, such as DDoS attacks or streaming piracy, ensuring that content remains secure and accessible.

Those are powerful advantages for running the business day-to-day, but there's arguably a bigger benefit on the long-term side. Retaining multi-CDN log data for months or years allows companies to perform the kinds of in-depth analyses that 90-day retention policies can't deliver. Uncovering longer-term trends and insights can illuminate future strategies, capacity planning, and cost management.

And of course, there's the holy grail of traffic steering and optimization. By analyzing multi-CDN data, businesses can make informed decisions on how to route traffic, avoiding congested paths and optimizing delivery based on current conditions.

Best Practices for Multi-CDN Monitoring

To effectively leverage multi-CDN, companies should adopt the following best practices:

Insist on Real-time Query-ability: This is essential because logs capture the state of live services, the interruption of which can damage a brand's revenue and reputation.

Combine Log Sources for a Unified View: Ingest data from all CDNs into a single platform to enable easy comparison and analysis. This reduces the need for complex query operations and ensures a comprehensive view of performance.

Standardize Log Data: Use real-time transformation to standardize log data across different CDNs. Consistent naming conventions make querying and analysis more straightforward and reliable.

Enrich Logs with Contextual Data: Enhance CDN logs with additional metadata to provide more context and improve diagnostic capabilities. This can include adding virtual fields, computing new metrics, or extracting specific data elements.

Keep Data Long-Term: Don't discard valuable multi-CDN data after 90 days. Retain it for longer periods to enable deeper insights, trend analysis, and better decision-making. Yes, there are monitoring solutions that enable you to keep all of your data and still pay less for storage than you paid when keeping only 90 days of data.

By following these best practices, businesses can fully capitalize on the advantages of multi-CDN, ensuring optimal performance, reliability, and security for their digital content delivery.

Admittedly, not all organizations will face the type of do-or-die moments described at the outset of this article. Nevertheless, a vast and growing number of companies across a wide variety of industries do depend on a complex, interconnected set of services, and, as the recent Microsoft/Crowdstrike fiasco demonstrated these services can be ground to a halt with a modest mistake. Clearly, log monitoring has never been more important. But log management solutions for today's businesses must take a new approach to observability — one that is real-time and can simplify the management of tremendous volumes of data streaming in from myriad sources.

Tony Falco is VP Marketing at Hydrolix

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Do You Need Multi-CDN Monitoring? Here's What You Need to Consider - Part 2

Tony Falco
VP Marketing
Hydrolix

In Part 1 of this two-part series, I defined multi-CDN and explored how and why this approach is used by streaming services, e-commerce platforms, gaming companies and global enterprises for fast and reliable content delivery. Multi-CDN offers unmatched benefits when it comes to redundancy, availability, load balancing, scalability, cost management, and improved user experiences on a global scale. Now, in Part 2 of the series, I'll explore one of the biggest challenges of multi-CDN: observability.

Why Don't All Observability Platforms Monitor Multi-CDN?

If your log monitoring platform is capable of spanning multiple CDNs, you might be in good shape. Some do, but most don't. Those that do have differing levels of sophistication. Let's break down some of the details you'll want to examine.

1. Data Volume and Variety: Multi-CDN generates vast amounts of data from multiple sources. In fact, it's so much data that many multi-CDN users don't even try to analyze it because the storage costs alone can be eye watering. For observability platforms to provide the value you expect, they need to ingest, process, and store all this data in very near real time. With legacy systems, it's almost always resource-intensive and costly.

2. Real-Time Data Transformation: Effective multi-CDN monitoring requires the ability to transform and standardize data in real time, a capability that many platforms lack. Without this, data analysis becomes cumbersome and less accurate.

3. Unified View and Analysis: To effectively monitor multi-CDN performance, businesses need a unified view that combines data from all CDNs. This requires sophisticated data aggregation and query capabilities, which not all platforms can provide.

4. Considerations: As mentioned above, storing and processing multi-CDN data long-term is expensive. Many platforms limit data retention to 90 days due to cost constraints, which restricts the ability to perform historical analysis and trend forecasting. Unfortunately, historical data is where much of the value lies. The ideal log monitoring solution is one that keeps all data hot at minimal cost.

Few log monitoring platforms are capable of meeting all these standards. As a result, many companies end up throwing away their valuable log data, losing out on insights that quite literally describe the state of their business.

When Your Multi-CDN Has Access to All the Data

So, in the perfect world where all the elements above are working, what can having access to comprehensive multi-CDN log data unlock for your businesses?

First, there's real-time visibility and alerting. With data from multiple CDNs ingested into a unified view, companies can monitor performance in real time, detect issues early, and respond proactively to prevent disruptions — disruptions that frustrate customers and reduce revenue. Detailed IP address analytics can help identify and mitigate security threats, such as DDoS attacks or streaming piracy, ensuring that content remains secure and accessible.

Those are powerful advantages for running the business day-to-day, but there's arguably a bigger benefit on the long-term side. Retaining multi-CDN log data for months or years allows companies to perform the kinds of in-depth analyses that 90-day retention policies can't deliver. Uncovering longer-term trends and insights can illuminate future strategies, capacity planning, and cost management.

And of course, there's the holy grail of traffic steering and optimization. By analyzing multi-CDN data, businesses can make informed decisions on how to route traffic, avoiding congested paths and optimizing delivery based on current conditions.

Best Practices for Multi-CDN Monitoring

To effectively leverage multi-CDN, companies should adopt the following best practices:

Insist on Real-time Query-ability: This is essential because logs capture the state of live services, the interruption of which can damage a brand's revenue and reputation.

Combine Log Sources for a Unified View: Ingest data from all CDNs into a single platform to enable easy comparison and analysis. This reduces the need for complex query operations and ensures a comprehensive view of performance.

Standardize Log Data: Use real-time transformation to standardize log data across different CDNs. Consistent naming conventions make querying and analysis more straightforward and reliable.

Enrich Logs with Contextual Data: Enhance CDN logs with additional metadata to provide more context and improve diagnostic capabilities. This can include adding virtual fields, computing new metrics, or extracting specific data elements.

Keep Data Long-Term: Don't discard valuable multi-CDN data after 90 days. Retain it for longer periods to enable deeper insights, trend analysis, and better decision-making. Yes, there are monitoring solutions that enable you to keep all of your data and still pay less for storage than you paid when keeping only 90 days of data.

By following these best practices, businesses can fully capitalize on the advantages of multi-CDN, ensuring optimal performance, reliability, and security for their digital content delivery.

Admittedly, not all organizations will face the type of do-or-die moments described at the outset of this article. Nevertheless, a vast and growing number of companies across a wide variety of industries do depend on a complex, interconnected set of services, and, as the recent Microsoft/Crowdstrike fiasco demonstrated these services can be ground to a halt with a modest mistake. Clearly, log monitoring has never been more important. But log management solutions for today's businesses must take a new approach to observability — one that is real-time and can simplify the management of tremendous volumes of data streaming in from myriad sources.

Tony Falco is VP Marketing at Hydrolix

Hot Topics

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...