Observability vs. monitoring: Why upgrade to observability?

Achieving success in cloud native environments takes a fine-tuned approach. It requires embracing the latest technologies to enable speed and flexibility while at the same time ensuring operations remain stable and reliable.

This balance is challenging to maintain. The shift towards a microservices architecture on container-based platforms enables rapid iteration and the agility to adapt swiftly to the changing demands of customers, which is essential. However, this agility comes with its own set of risks.

Every introduction of a new tool, modification of a process, or alteration in an application or infrastructure component carries the potential to disrupt your environment. The questions of what has been impacted and where loom large. Due to the inherent complexities and numerous variables present in cloud native systems, identifying and resolving issues promptly can be a formidable task.

Additionally, the same challenges that your DevOps and site reliability engineering (SRE) teams have long confronted are often amplified in cloud native environments:

Human error: Over the past three years, 42% of enterprises have encountered downtime because of human mistakes.

External threats: In the last year alone, 40% of worldwide companies reported experiencing a data breach in their cloud systems.

Delayed detection and remediation: Limited visibility in complex cloud and hybrid environments prolongs downtime as it becomes more challenging to quickly restore services.

Poor app performance: A delay of merely three seconds leads to half of the prospective customers abandoning a website.

These issues have substantial implications for businesses. According to a recent survey by ITIC, 91% of businesses report that downtime costs exceed $300,000 per hour, with nearly half (44%) estimating losses over $1 million per hour.

While traditional application monitoring tools have been effective in managing such problems in more stable and consistent VM-based  environments both on-premise and in the cloud, their efficacy often does not translate well to the complexities of cloud native environments.

Now, before we explore the differences between monitoring and observability, let’s take a look at these processes on their own.

Monitoring is the ongoing consistent data collection and ongoing analysis  and documentation of system activities. It utilizes tools that gather information on the performance of applications. This data is typically then conveyed to a dashboard for analysis and could potentially prompt notifications if the system crosses predefined limits.

Monitoring ensures continuous oversight of your applications’ health, enabling proactive responses to identified vulnerabilities.

Observability is an extension of the established concept of monitoring. Instead of relying on vendor-supplied agents for data collection, observability shifts the responsibility to developers and application owners to instrument their own services and have them produce the data that is most useful for describing their systems. The observability tool must be able to collect, analyze, and display the heterogeneous data, giving developers the ability to go through a troubleshooting workflow.

The primary distinction between monitoring and observability lies in who they are built to serve. Monitoring tools are built for the operations team to oversee and enhance the performance of infrastructure applications. Observability, on the other hand, is built into the DevOps lifecycle and is a developer tool for troubleshooting and accelerating application development in a cloud native environment.

In advanced use cases, the questions observability seeks to answer revolve around the impact on users and customers, how to enhance flexibility in iterations, and how to accelerate the delivery of benefits to the business more effectively. It adopts a holistic perspective to ensure systems remain operational and efficient.

Both monitoring and observability derive from control theory, a mathematical framework that leverages feedback from complex systems to modify their behavior, allowing operators to achieve specific objectives. The central concept here is that by observing the external “outputs” of a system, users can deduce what is occurring internally.

While monitoring tools are essential, they are insufficient by themselves especially in cloud native and DevOps environments. Developers require observability to quickly remediate issues and get back to their core responsibility – delivering software. 

Given that every modern business is now essentially a software business, it’s critical to recognize the direct link between the technology they implement and their business success. When executed successfully, observability can  enhance developer productivity,  accelerate time to market, and enable superior customer experiences.

Observability represents an advancement beyond traditional monitoring tools. It offers a strategic edge in the competitive cloud native market by enabling better control over data-related costs, quicker remediation times, and minimized system downtime.

To better understand observability, here’s an overview of the four types of telemetry data collected by observability tools:

Metrics: Measurements recorded from a system, often over a designated time period. Metrics help businesses identify potential issues within their systems.

Logs: Timestamped documents detailing one or more events. Logs provide insights into what the issue might be and the reasons behind it.

Distributed Traces: Track the sequence of events that transpire across the pathway of a request. Distributed traces are instrumental in pinpointing the location of a problem, aiding in the troubleshooting process.

Events: An event is a discrete change to a system, a workload, or an observability platform.

Although these four telemetry types are fundamental to achieving observability, there is an evolving perspective that observability extends beyond mere data collection and analysis.

Another way to understand observability is to focus on its outcomes: know, triage, and understand. This perspective shifts focus, prioritizing the mitigation of impacts on users and customers as quickly as possible, diverging from traditional definitions that primarily concentrate on data mechanics.

Conceptualizing the phases of observability:

Know: The initial step is recognizing that a problem exists. Similar to detecting a fire by the smell of smoke, in a cloud native environment, the first indication of an issue often comes in the form of an alert or an on-call notification. This is the trigger that initiates the remediation process.

Triage: The next step involves mobilizing the necessary resources to address the issue and determining immediate next steps. Do you first call the fire department, or look for your fire extinguisher?  Time is of the essence during the triage stage.

Remediate: This phase is akin to extinguishing the fire. The primary goal here is to restore normal operations for users and customers as swiftly as possible. Only after these immediate concerns are managed do you shift focus to investigating the underlying causes of the issue.

Understand: The final phase is about exploring the reasons behind the problem, which occurs once the immediate issue is resolved. During this stage, you analyze the cause and integrate what you’ve learned back into the system to prevent future occurrences. This proactive approach helps refine and strengthen the system against similar disruptions.

Leading observability tools often share certain characteristics. Here are three of the key elements to look for when evaluating observability platforms.

1. Ingest a wide variety of heterogeneous data

The data feeding into observability tools such as metrics, logs, events, and traces comes from a wide array of sources and instrumentation. This data, crucial for providing visibility into applications and infrastructure, originates from various environments including apps, services, cloud platforms, mobile apps, or containers. Additionally, this data is available in multiple formats, including open source, industry standard, or proprietary.

With the increasing variety of both proprietary and open source data sources, it’s essential that observability tools are capable of collecting and integrating data from all types of instrumentation to comprehensively understand the environment.

Therefore, DevOps and SRE teams need an observability platform that ensures full interoperability of all data, regardless of its origin or format. This capability is vital for maintaining a complete and actionable overview of system performance.

2. Observability platforms enhance data with ample context 

Just as context is crucial in everyday life for understanding the world around us, it plays a similar role in cloud native systems. Without context, interpreting the daily influx of data would be as challenging in digital environments as it is in real life. Factors like tone, setting, and even external conditions such as weather or personal circumstances like hunger, significantly influence how we process information.

In the realm of observability, context enriches telemetry data, which provides critical insights into the internal workings of applications and infrastructure. Contextual intelligence is equally vital; it allows for deeper understanding and interpretation of data.

For instance, understanding how a system performed in the past, the specific configuration of the server it runs on, or any anomalies in the workload at the time of an issue can be crucial. Advanced observability platforms offer the capability to enhance data with such context, helping to filter out irrelevant information, pinpoint actual problems, and streamline the troubleshooting process. This is more critical that a simple “single pane of glass,” and the ability to correlate relevant data with this enriched context is what will ultimately drive faster time to remediation.

3. Observability tools that control the data explosion

A cloud native environment’s observability data grows at an enormous rate. With more entities creating more data at an increasing rate, most organizations are running into two major problems:

1. Organizations are spending more and more money on their observability tools.
2. Engineers are struggling to find the data to quickly solve problems.

If this increase in data actually helped developers solve problems faster, organizations might pay an increased tax on this data. But in practice, the opposite occurs: High data volumes actually slow down developers. You need to look for an observability tool that has features that can tame the high data volumes, separates out the valuable from the useless data, and lets you only retain what’s useful; all of this must be done without sacrificing outcomes.

The first step is to understand the data that’s coming in; see what’s being used and what isn’t; and what data has unnecessary duplication or dimensionality. Second, you need to determine what you want to keep and what you want to discard – ideally at an application or a service level – because not all services are of equal importance and you may want to keep higher granularity data for more critical services.

Third, you need to be able to allocate costs to the teams that are driving the consumption, in a centralized governance model, to ensure accountability for usage.

Observability helps cloud native businesses in many ways. Here are four in particular that distinguish observability from basic monitoring:

Foster a culture of continuous innovation

Observability tools quickly highlight what is effective and what isn’t, facilitating ongoing enhancements in performance, reliability, and efficiency that directly benefit the business. As your understanding of how technology supports your business objectives deepens, you can continually refine your infrastructure and services. This ensures they not only meet customer expectations but also help prevent downtime or disruptions to service.

Invest strategically in new technologies

In today’s landscape, engineering teams manage more than just physical computing hardware; they are also in charge of vast amounts of data and cloud infrastructure. By monitoring data related to business performance, internal processes, and customer interactions, IT departments can more effectively prioritize responses to on-call alerts and specific system outages. This comprehensive overview enables IT to supply management with the crucial data needed to make informed decisions regarding investments in new software, data collection methods, and cloud services.

Gain real-time insights into digital business performance

By integrating various levels and types of data into dashboards, teams gain a clear understanding of the current state of their environment and its impact on the business. This information may include standard telemetry data, feedback on resource optimization, business-oriented KPIs, and metrics related to user experience. The ability to collect this data in real time enables prompt responses to incidents, potentially addressing issues before they impact customers.

Speed up the deployment of cloud native applications

Agile workflows empower developers to rapidly develop, test, iterate, and refine, accelerating the production of cloud native applications while minimizing errors. However, the frequent changes inherent in these processes can introduce new challenges and increase deployment risks. Leveraging observability, DevOps teams can diagnose and debug issues more efficiently. Continuous delivery and continuous integration (CI/CD) processes further decrease the time between feature testing and deployment, enhancing overall productivity and reducing potential downtime.

DevOps engineers and SREs who oversee cloud native environments encounter daily challenges. They must continuously unravel the complexities of distributed systems, pinpoint elusive issues, and accelerate troubleshooting processes to prevent the business from suffering due to digital disruptions or failures.

While essential, monitoring tools alone are inadequate for today’s businesses, which must integrate technology closely with business outcomes and maintain robust data management to improve productivity, speed to market, and customer service. Observability extends beyond basic monitoring, offering key advantages like cost efficiency, faster problem-solving, and minimized downtime. Companies that do not embrace these practices risk falling behind in a highly competitive environment.

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