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Why You Should Use Packet Analysis to Complement NetFlow When Monitoring Network Performance

Chris Bloom

Most organizations understand that centralized network monitoring is vital to maintaining the health of critical infrastructure and applications. And while solutions using NetFlow undoubtedly help gain perspective into capacity planning, trend analysis, and utilization, they lack the important precision of packet-based analytics tools that provide root-cause analysis for application performance, latency, TCP/IP or VoIP problems. Both monitoring technologies have their advantages and ideal use cases, so let's see how enterprises can maximize existing infrastructure and equipment investments by using packet analytics to complement NetFlow.

Evolution of Network Monitoring Technologies

First up is NetFlow. This is a well-known, well-established standard that provides conversational information about network status. Compared with even older protocols like SNMP, NetFlow offers greater precision, delivering data at intervals of around 1 second, depending on the equipment being monitored. NetFlow also has the advantage when it comes to providing a good global view of a network. This can be extremely helpful when monitoring the network's general health.

Next, let's turn to packed-based analytics. This breed of network monitoring solution delivers a much higher troubleshooting value to NetOps teams thanks to its data granularity and access to raw data. Since it is packet based, it is very precise, and interval times are often as short as a few nanoseconds. On top of that, the data is completely based on the original payload, so it isn't abbreviated or compiled.


What's the Difference Between Flow- and Packet-Based Analytics?

The key benefit of packet-based solutions is that they can provide much more information that can be used in network diagnostics. If there is a problem, the packet-based approach is completely passive, so it doesn't burden the network or interfere with existing operations or services. As you can imagine, this is very important, especially because nobody wants to exacerbate existing problems by piling on more network traffic.

NetFlow data, which typically comes from Layer 3 devices like routers and firewalls, provides good information about traffic volume between devices. But if you need to use multiple ports, NetFlow is at a disadvantage. This is where packet-based analytics come into their own. Packet analysis allows users to drill down and discover information about how the network is behaving, not just whether it's operating well. All of the packets and all of the information is there in the packets, so it's also going to be 100 percent accurate. And the final advantage is that packet-based analysis can be implemented with very little impact on the network, while supporting monitoring and troubleshooting simultaneously.

When it comes to troubleshooting, Flow-based technology is useful only up to Layer 3 (and occasionally Layer 4) so at least we can see where data traffic is being generated. When the NetOps team starts to get trouble tickets about a slow network or a CRM that's unable to save any records (for example), they need to start looking at the root cause. In this scenario, NetFlow would reveal that traffic is going between the client and the server, and that it's running on a specific port. It could also tell you what volume of traffic is produced by each of the clients. In other words, you could verify simple problems like whether the server is up and running and whether the port is operational.

The key here is that NetFlow alone isn't adequate in a modern network setting. It struggles to identify any activity associated with content delivery networks and applications that use multiple TCP or UDP ports. It also has no visibility into the payload or its contents. You may be able to see that a server has an issue, but that's far from definitive.

Take a look at the screenshot below, taken from a real use case. In this situation, a client is unable to get a response from a server, and its task is canceled. By investigating the reason for this problem, the packet-based solution quickly identifies the issue and shows the cause. In the text box at the bottom we see a message: “Your server command (process id 169) was deadlocked with another process and has been chosen as deadlock victim. Re-run your command.” This reference code tells us that the error was generated when two tasks concurrently requested access to the same resource. Armed with this information, the network team quickly determines that the problem is with the application, not the network, and provides the application team with actionable data to directly address the issue.


Packet-based analysis has been designed specifically to reveal the “how” of the network. Rather than being about just the volume of traffic, these solutions expose vital details about performance and application response. Users can compare network latency with application latency. They can see the efficiency of TCP communications on their network. They can evaluate the performance of VoIP and video over the network and determine if these real-time protocols are prioritized correctly. None of this can be achieved with NetFlow or its derivatives.

To help make my point, here are five common questions that can be solved when packet-based analysis is used in tandem with Netflow:

■ Is it the network or the application?

■ Is the issue isolated to a single user, a single server, or the network overall?

■ Are critical applications using network resources efficiently?

■ Is my network correctly configured for unified communications, and are unified communications co-existing with other network transactions?

■ Are critical functions, for example user authentication, failing due to protocol issues?

NetFlow certainly has its place in the network monitoring hierarchy, but its limitations make it less than ideal in many situations. For most network professionals, having access to packet data is a no-brainer and significantly accelerates mean-time-to-resolution (MTTR). The challenge is in learning how to balance the way we use these tools in our approach to network monitoring.

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Why You Should Use Packet Analysis to Complement NetFlow When Monitoring Network Performance

Chris Bloom

Most organizations understand that centralized network monitoring is vital to maintaining the health of critical infrastructure and applications. And while solutions using NetFlow undoubtedly help gain perspective into capacity planning, trend analysis, and utilization, they lack the important precision of packet-based analytics tools that provide root-cause analysis for application performance, latency, TCP/IP or VoIP problems. Both monitoring technologies have their advantages and ideal use cases, so let's see how enterprises can maximize existing infrastructure and equipment investments by using packet analytics to complement NetFlow.

Evolution of Network Monitoring Technologies

First up is NetFlow. This is a well-known, well-established standard that provides conversational information about network status. Compared with even older protocols like SNMP, NetFlow offers greater precision, delivering data at intervals of around 1 second, depending on the equipment being monitored. NetFlow also has the advantage when it comes to providing a good global view of a network. This can be extremely helpful when monitoring the network's general health.

Next, let's turn to packed-based analytics. This breed of network monitoring solution delivers a much higher troubleshooting value to NetOps teams thanks to its data granularity and access to raw data. Since it is packet based, it is very precise, and interval times are often as short as a few nanoseconds. On top of that, the data is completely based on the original payload, so it isn't abbreviated or compiled.


What's the Difference Between Flow- and Packet-Based Analytics?

The key benefit of packet-based solutions is that they can provide much more information that can be used in network diagnostics. If there is a problem, the packet-based approach is completely passive, so it doesn't burden the network or interfere with existing operations or services. As you can imagine, this is very important, especially because nobody wants to exacerbate existing problems by piling on more network traffic.

NetFlow data, which typically comes from Layer 3 devices like routers and firewalls, provides good information about traffic volume between devices. But if you need to use multiple ports, NetFlow is at a disadvantage. This is where packet-based analytics come into their own. Packet analysis allows users to drill down and discover information about how the network is behaving, not just whether it's operating well. All of the packets and all of the information is there in the packets, so it's also going to be 100 percent accurate. And the final advantage is that packet-based analysis can be implemented with very little impact on the network, while supporting monitoring and troubleshooting simultaneously.

When it comes to troubleshooting, Flow-based technology is useful only up to Layer 3 (and occasionally Layer 4) so at least we can see where data traffic is being generated. When the NetOps team starts to get trouble tickets about a slow network or a CRM that's unable to save any records (for example), they need to start looking at the root cause. In this scenario, NetFlow would reveal that traffic is going between the client and the server, and that it's running on a specific port. It could also tell you what volume of traffic is produced by each of the clients. In other words, you could verify simple problems like whether the server is up and running and whether the port is operational.

The key here is that NetFlow alone isn't adequate in a modern network setting. It struggles to identify any activity associated with content delivery networks and applications that use multiple TCP or UDP ports. It also has no visibility into the payload or its contents. You may be able to see that a server has an issue, but that's far from definitive.

Take a look at the screenshot below, taken from a real use case. In this situation, a client is unable to get a response from a server, and its task is canceled. By investigating the reason for this problem, the packet-based solution quickly identifies the issue and shows the cause. In the text box at the bottom we see a message: “Your server command (process id 169) was deadlocked with another process and has been chosen as deadlock victim. Re-run your command.” This reference code tells us that the error was generated when two tasks concurrently requested access to the same resource. Armed with this information, the network team quickly determines that the problem is with the application, not the network, and provides the application team with actionable data to directly address the issue.


Packet-based analysis has been designed specifically to reveal the “how” of the network. Rather than being about just the volume of traffic, these solutions expose vital details about performance and application response. Users can compare network latency with application latency. They can see the efficiency of TCP communications on their network. They can evaluate the performance of VoIP and video over the network and determine if these real-time protocols are prioritized correctly. None of this can be achieved with NetFlow or its derivatives.

To help make my point, here are five common questions that can be solved when packet-based analysis is used in tandem with Netflow:

■ Is it the network or the application?

■ Is the issue isolated to a single user, a single server, or the network overall?

■ Are critical applications using network resources efficiently?

■ Is my network correctly configured for unified communications, and are unified communications co-existing with other network transactions?

■ Are critical functions, for example user authentication, failing due to protocol issues?

NetFlow certainly has its place in the network monitoring hierarchy, but its limitations make it less than ideal in many situations. For most network professionals, having access to packet data is a no-brainer and significantly accelerates mean-time-to-resolution (MTTR). The challenge is in learning how to balance the way we use these tools in our approach to network monitoring.

Hot Topics

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...