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Network Observability vs. DevOps Observability

Shamus McGillicuddy

If you work in an IT organization, you've likely heard the term "observability" lately. If you're a DevOps pro, you probably know exactly what vendors are talking about when they use the term. If you're a NetOps pro, you might be scratching your head.

DevOps Knows Observability

The DevOps community is very familiar with the observability concept. It refers to the ability to understand the internal state of a system by measuring its external outputs. In DevOps, the system is the application, and the outputs are metrics, logs, and traces. DevOps pros know how to navigate messaging from application performance management and cloud monitoring vendors to find solutions that can deliver the observability they need.

More recently, network monitoring vendors have started talking about network observability. Here is where things get fuzzy. In my opinion, DevOps observability and network observability are not interchangeable. Why would they be?

DevOps teams want to understand the state of applications and the infrastructure on which they reside. NetOps teams need to understand a much larger universe of networks, from the cloud to the user edge.

Both DevOps observability and network observability refer to the need to understand the internal state of a system, but that need to understand is only a problem statement. The solution to that problem is where the differences occur.

Does Anyone Have Network Observability?

First, most NetOps teams care about application performance. They want to collect data from the application environment if they can, such as hypervisors and containers. But they don't stop there. They need to monitor data center networks, wide-area networks (WANs), and campus and branch networks. More recently, they've had to worry about home office networks.

Each network they monitor has become more complex. The data center network has been virtualized and partially extended into the public cloud. The WAN has hybridized, with a mix of managed WAN connectivity, public internet, and 4G/5G. Office networks are a mix of ethernet and Wi-Fi, connected via home internet.

A network observability system must monitor and analyze an extremely diverse and ever-growing data set to understand end-to-end network state. A NetOps team might use five, ten, or even fifty tools to monitor a network by collecting packets, flows, device logs, device metrics, test data, DNS logs, routing table changes, configuration changes, synthetic traffic, and more.

It's a lot to keep track of, and it's hard to find a single tool that can handle it all. In fact, my new research on the concept of network observability found that 83% of IT organizations are interested in streaming data from their network observability tool(s) to a central data lake. Why? Nearly half of them believe a data lake will help them correlate network data across their tools.

Earlier, I wrote that network observability is a bit "fuzzy." I'd argue that it's fuzzy because the problem of network observability is much bigger and more complex than DevOps observability. It may prove impossible for any single tool to solve this problem. That's perfectly okay. But IT organizations must keep this in mind and take a comprehensive approach to network operations tools as they steer toward the promise of network observability.

To learn more about network observability, check out EMA's November 9 webinar, which will highlight market research findings on the topic.

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Network Observability vs. DevOps Observability

Shamus McGillicuddy

If you work in an IT organization, you've likely heard the term "observability" lately. If you're a DevOps pro, you probably know exactly what vendors are talking about when they use the term. If you're a NetOps pro, you might be scratching your head.

DevOps Knows Observability

The DevOps community is very familiar with the observability concept. It refers to the ability to understand the internal state of a system by measuring its external outputs. In DevOps, the system is the application, and the outputs are metrics, logs, and traces. DevOps pros know how to navigate messaging from application performance management and cloud monitoring vendors to find solutions that can deliver the observability they need.

More recently, network monitoring vendors have started talking about network observability. Here is where things get fuzzy. In my opinion, DevOps observability and network observability are not interchangeable. Why would they be?

DevOps teams want to understand the state of applications and the infrastructure on which they reside. NetOps teams need to understand a much larger universe of networks, from the cloud to the user edge.

Both DevOps observability and network observability refer to the need to understand the internal state of a system, but that need to understand is only a problem statement. The solution to that problem is where the differences occur.

Does Anyone Have Network Observability?

First, most NetOps teams care about application performance. They want to collect data from the application environment if they can, such as hypervisors and containers. But they don't stop there. They need to monitor data center networks, wide-area networks (WANs), and campus and branch networks. More recently, they've had to worry about home office networks.

Each network they monitor has become more complex. The data center network has been virtualized and partially extended into the public cloud. The WAN has hybridized, with a mix of managed WAN connectivity, public internet, and 4G/5G. Office networks are a mix of ethernet and Wi-Fi, connected via home internet.

A network observability system must monitor and analyze an extremely diverse and ever-growing data set to understand end-to-end network state. A NetOps team might use five, ten, or even fifty tools to monitor a network by collecting packets, flows, device logs, device metrics, test data, DNS logs, routing table changes, configuration changes, synthetic traffic, and more.

It's a lot to keep track of, and it's hard to find a single tool that can handle it all. In fact, my new research on the concept of network observability found that 83% of IT organizations are interested in streaming data from their network observability tool(s) to a central data lake. Why? Nearly half of them believe a data lake will help them correlate network data across their tools.

Earlier, I wrote that network observability is a bit "fuzzy." I'd argue that it's fuzzy because the problem of network observability is much bigger and more complex than DevOps observability. It may prove impossible for any single tool to solve this problem. That's perfectly okay. But IT organizations must keep this in mind and take a comprehensive approach to network operations tools as they steer toward the promise of network observability.

To learn more about network observability, check out EMA's November 9 webinar, which will highlight market research findings on the topic.

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...