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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

Gartner identified the top data and analytics (D&A) trends for 2025 that are driving the emergence of a wide range of challenges, including organizational and human issues ...

Traditional network monitoring, while valuable, often falls short in providing the context needed to truly understand network behavior. This is where observability shines. In this blog, we'll compare and contrast traditional network monitoring and observability — highlighting the benefits of this evolving approach ...

A recent Rocket Software and Foundry study found that just 28% of organizations fully leverage their mainframe data, a concerning statistic given its critical role in powering AI models, predictive analytics, and informed decision-making ...

What kind of ROI is your organization seeing on its technology investments? If your answer is "it's complicated," you're not alone. According to a recent study conducted by Apptio ... there is a disconnect between enterprise technology spending and organizations' ability to measure the results ...

In today’s data and AI driven world, enterprises across industries are utilizing AI to invent new business models, reimagine business and achieve efficiency in operations. However, enterprises may face challenges like flawed or biased AI decisions, sensitive data breaches and rising regulatory risks ...

In MEAN TIME TO INSIGHT Episode 12, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses purchasing new network observability solutions.... 

There's an image problem with mobile app security. While it's critical for highly regulated industries like financial services, it is often overlooked in others. This usually comes down to development priorities, which typically fall into three categories: user experience, app performance, and app security. When dealing with finite resources such as time, shifting priorities, and team skill sets, engineering teams often have to prioritize one over the others. Usually, security is the odd man out ...

Image
Guardsquare

IT outages, caused by poor-quality software updates, are no longer rare incidents but rather frequent occurrences, directly impacting over half of US consumers. According to the 2024 Software Failure Sentiment Report from Harness, many now equate these failures to critical public health crises ...

In just a few months, Google will again head to Washington DC and meet with the government for a two-week remedy trial to cement the fate of what happens to Chrome and its search business in the face of ongoing antitrust court case(s). Or, Google may proactively decide to make changes, putting the power in its hands to outline a suitable remedy. Regardless of the outcome, one thing is sure: there will be far more implications for AI than just a shift in Google's Search business ... 

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Chrome

In today's fast-paced digital world, Application Performance Monitoring (APM) is crucial for maintaining the health of an organization's digital ecosystem. However, the complexities of modern IT environments, including distributed architectures, hybrid clouds, and dynamic workloads, present significant challenges ... This blog explores the challenges of implementing application performance monitoring (APM) and offers strategies for overcoming them ...