What does it mean to be observable?

What is observability? Not just logs, metrics and traces

What is observability?

In IT and cloud technology, measurability is how to assess a system’s current state depending on the statistics it generates, such as logs, metrics, and traces.

Telemetry produced through instrumentation from endpoints and services in your multi-cloud computing setups is used for observability. Every component of hardware, software, and cloud infrastructure, as well as every container, open-source tool, and microservice, generates records of every activity in these modern systems. The purpose of observability is to comprehend what’s going on across all of these settings and among the technologies to spot and fix problems to keep your systems running smoothly and your customers satisfied.

Organizations typically use a combination of instrumentation approaches, including open-source instrumentation tools like OpenTelemetry, to enable observability.

Many businesses utilize an observability solution to detect and analyze the impact of events on their operations, software development lifecycles, application security, and end-user experiences.

As cloud-native settings have grown more complicated and the probable root causes for a failure or anomaly have become more difficult to detect, observability has become more critical in recent years. As teams begin to collect and engage with observability data, they see how beneficial it is to the entire business, not just IT.

Because cloud services are built on a distributed and dynamic architecture, observability can also relate to the software tools, and methods companies employ to understand cloud performance data. Although some individuals mistake observability with sophisticated application performance monitoring (APM), there are a few crucial considerations to consider when comparing the two.

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What is the difference between monitoring and observability?

Is observability just another name for monitoring? In a nutshell, no. While validity and monitoring are related concepts that might complement one other, they are separate concepts.

You normally preconfigure dashboards in a monitoring scenario to alert you to performance concerns you expect to observe later. On the other hand, these dashboards are predicated on the premise that you can foresee the types of difficulties you’ll face ahead of time.

Because cloud-native systems are dynamic and complicated, they don’t lend themselves well to this monitoring form. As a result, you cannot know what problems might occur in advance.

In an observability situation, where an environment has been wholly instrumented to provide complete observability data, you may flexibly investigate what’s going on and quickly figure out the root cause of issues you might not have anticipated.

Why is observability important?

Observability helps enterprise cross-functional teams understand and respond to questions about what’s happening in massively dispersed systems. Observability enables you to identify what is slow or faulty and what has to be addressed to improve performance. With an observability solution in place, teams can get proactive notifications about problems and fix them before affecting users.

Because current cloud infrastructures are dynamic and constantly expanding in scale and complexity, most problems aren’t understood or monitored. Observability tackles the “unknown unknowns” problem by allowing you to understand new types of trials automatically and continuously as they arise.

The observability of artificial intelligence is equally essential for IT operations (AIOps).

Organizations are seeking ways to incorporate AIOps into their operations. As more firms adopt cloud-native architectures, this strategy uses artificial intelligence to automate more processes throughout the DevSecOps life cycle. AI uses everything from data collection to evaluate what’s going on throughout the entire technological stack. It can provide trustworthy answers for automated application monitoring, testing, continuous delivery, application security, and incident response for your firm.

Observability’s advantages don’t confine to IT applications. You’ll have a vital window into the commercial impact of your digital services once you start collecting and evaluating observability data. You can use this access to boost conversions and ensure that software releases align with corporate goals. Measure your customer experience SLOs’ progress, or prioritize business decisions based on the most critical aspects.

An observability system uses synthetic and real-user monitoring to assess user experience data. You can identify issues before your users do and create better user experiences based on real-time feedback.

Benefits of observability

IT teams, organizations, and end-users all benefit from observability somehow. Here are some of the applications that observability makes possible:

1. It is monitoring the performance of an application. Enterprises can immediately discover and rectify application performance issues with full end-to-end observability. Those that arise in cloud-native and microservices environments, in particular. It can also automate more tasks with the help of an enhanced observability solution. It was increasing the efficiency and innovation of the Ops and Apps teams.
2. SRE. An application’s and its supporting infrastructure’s observability is a critical feature. Not just as a result of utilizing new technologies. Architects and developers of software must design it so that it can observe. Teams may then use and analyze the observable data during the software development life cycle to create better, more secure, and resilient apps.
3. Infrastructure, cloud, and Kubernetes monitoring: Infrastructure and operations (I&O) teams can use an observability solution’s enhanced context to improve software uptime and achievement, reduce the time it takes to pinpoint as well as resolve issues, detect cloud latency issues as well as optimize cloud resource utilization, and better manage their Kubernetes environments and new cloud-based architectures.
4. The viewpoint of the user. A great user experience can improve its reputation and revenue, giving it a competitive advantage. Can improve customer happiness and retention by identifying and resolving issues before the end-user recognizes them and implementing improvements before they’re ever requested. It can also use Real-time playback to improve the user experience. By offering a clear look into the end-user experience as they see it, everyone can instantly agree on where they should make changes.
   
5. Organizations can combine business context with full-stack application analytics and performance to understand the real-time business effect. Improve conversion optimization, ensure that software releases align with company objectives, and satisfy internal and external SLAs. Teams can use observability to gain deeper insights into the apps they develop and automate testing and CI/CD procedures to deliver higher-quality code more quickly. As a result, organizations will spend less time in war rooms and pointing fingers. It is advantageous not just in terms of productivity but also in strengthening the positive working relationships required for effective collaboration.

These organizational changes pave the way for more innovation and digital transformation. And, perhaps most crucially, the end-user benefits in the form of a superior user experience.

How do you make a system observable?

If you’ve read anything about observability, you’re presumably aware that it builds on three pillars. Log measurements, metrics, and distributed traces are essential for success. On the other hand, raw telemetry from back-end programs does not provide a complete view of how your systems are doing.

Neglecting the front-end perspective can impact, and even be misleading, your app’s actual performance of your apps. Also, real-world infrastructure for real-world users. To avoid blind spots, IT teams must supplement telemetry gathering with user-experience data, as part of the three-pillar approach:

1. Logs are structured or unstructured text records of discrete events that occurred at a particular time. 
2. Metrics are numerical numbers expressed as counts or measures frequently calculated or aggregated over time. Infrastructure, hosts, services, cloud platforms, and external sources are just some of where metrics might come from. 
3. It is tracing on a large scale. This diagram shows how can use services to link and display a transaction or request activity as it flows through applications, including data at the code level.
4. The user’s perspective. It adds the outside-in user perspective of a specific digital experience on an application to standard observability telemetry even in pre-production scenarios.

Why the three pillars of observability aren’t enough

Gathering data is simply the beginning. To obtain genuine observability into your environment, having access to the correct logs, analytics, and traces isn’t enough. Only until you can use that telemetry data to achieve the ultimate goals of increasing end-user experience and business outcomes can you truly declare you’ve accomplished the goal of observability.

Organizations can also use various observability features to monitor their environments. Open-source technologies, such as OpenTelemetry, have become the de facto standard for telemetry data collection in cloud environments. These open-source solutions improve the observability of cloud-native applications. They make it easy for developers and operations teams to get a consistent view of application health from various perspectives.

Following is an example of how can utilize accurate user monitoring to provide real-time visibility into the user experience. The path of a single request and the lessons learned from each interaction with each service along the route. To observe this event, you can employ synthetic monitoring or even a recording of the actual session. These features broaden telemetry by including data about APIs, third-party services, browser bugs, user demographics, and application performance from the user’s perspective. It enables IT, DevSecOps, and SRE teams to see a request’s whole end-to-end path and obtain real-time insight into system health.

They can then troubleshoot regions of declining health before they impact application performance. They can also recover from failures more quickly and better grasp the user experience.

While IT organizations have the best intentions and strategies, they frequently underestimate the ability of already overburdened teams to continuously watch, comprehend, and act on an impossible quantity of data and insights. Although there are numerous difficult hurdles involved with observability, firms that succeed in overcoming these obstacles will find it worthwhile.

What are the challenges of observability?

Observability has always been a difficulty, but cloud complexity and the rapid development speed have made it a pressing concern for businesses. When microservices and containerized applications are involved, cloud environments create significantly more telemetry data. They also generate considerably more telemetry data than teams have ever had to evaluate previously. Finally, the rapidity with which all of this data is developed makes keeping up with the flow of information, let alone accurately interpreting it in time to solve a performance issue, all the more difficult.

Organizations commonly face some severe issues when it comes to visibility:

• Multiple agents, disparate data sources, and walled monitoring tools make it challenging to comprehend interdependencies across apps, clouds, and digital channels like web, mobile, and IoT. 
• Volume, velocity, variety, and complexity are all factors to consider. It’s practically impossible to extract answers from the massive amounts of raw data created by every component in ever-changing modern cloud platforms like AWS, Azure, and Google Cloud Platform (GCP). It is also true for Kubernetes and containers, which may be started and stopped in seconds. 
• Hand-configured instrumentation and configuration. IT employees must manually instrument and update code for each new type of material or agent. They devote more work to establishing detectability than innovating based on significant data insights.
• Developers cannot see or understand how real consumers will interact with programs and infrastructure. Even before deploying code into production, load testing requirements.
• Teams from the application, operations, infrastructure, development, and digital experience are bright in troubleshooting and finding the root cause of problems. They waste time guessing and attempting to decipher data to get answers.

Then there’s the problem of many vendors and tools. While a single device may provide observability into a specific region of an organization’s application architecture, it may not offer total observability across all apps and systems that can affect application performance.

Furthermore, not all types of telemetry data are equally beneficial for identifying the source of an issue or estimating its influence on the user experience. As a result, teams tasks with the time-consuming effort of combing through many solutions for answers and meticulously deciphering telemetry data when they might be putting their knowledge to work immediately to solve the problem. Teams can acquire answers and troubleshoot issues faster if they have a single source of truth.

The importance of a single source of truth

Organizations need a single source of truth to gain broad observability throughout their application architecture and precisely diagnose the root reasons for performance issues. When businesses have a single platform to manage cloud complexity, collect all necessary data, and evaluate it using artificial intelligence. They can swiftly identify the source of any problem, whether it’s in the application or the underlying architecture.

Teams can use a single source of truth to:

• Rather than relying on IT staff to piece together a picture of what transpired from various data sources. Transform gigabytes of telemetry data into real-world solutions.
• Obtain essential insight into parts of the infrastructure that they might not have otherwise been able to view.
• To expedite the troubleshooting process, work together. Because of the greater visibility, the organization can move faster than it could with traditional monitoring systems.

Making observability actionable and scalable for IT teams

Must achieve observability for resource-constrained teams to act on the abundance of real-time telemetry data. To prevent business-impacting problems from spreading or even arising in the first place. Here are some ideas for making observability scalable and actionable. 

• Recognize the context as well as the topology. It necessitates instrumenting in highly dynamic, multi-cloud settings with possibly billions of interconnected components to build an understanding of correlations between each interdependency. Real-time topology maps are possible thanks to rich context metadata. It provides a deeper understanding of vertical and horizontal causal relationships between services, processes, and hosts.
• Should implement continuous automation. Every system component is constantly discovered, instrumented, and baselined, diverting IT resources away from manual configuration work. And then, there are value-added innovation projects that place a premium on knowing what matters. So that constrained teams can achieve more with less, the observability becomes “always-on” and scalable. 
• Make AIOps judgments that are indeed AIOps. Extensive AI-driven fault-tree analysis combined with code-level visibility allows for quick identification of the root cause of issues without time-consuming human experiments, guesswork, or correlations. Furthermore, causation-based AI may automatically recognize any odd change points, allowing “unknown unknowns” to be discovered and tracked. These actionable insights enable DevOps and SRE teams to respond faster and accurately. 
• Encourage an open ecosystem: This broadens the scope of observability to incorporate external data sources like OpenTelemetry. Technologies that enable topological mapping, automated discovery, instrumentation, and actionable reactions are necessary for observability at scale. OpenTelemetry makes it easier to collect and use telemetry data. 

An AI-driven solution-based approach makes observability actionable by addressing the issues associated with cloud complexity. An observability solution makes it easier to decipher the massive flood of telemetry data coming from many sources at ever-faster speeds. Teams can rapidly and precisely detect root causes of issues before they result in impaired application performance or, if a breakdown has already happened, accelerate their time to recovery with a single source of truth. 

Through end-to-end distributed tracing across serverless platforms, Kubernetes environments, microservices, and open-source solutions, advanced observability further increases application availability. Teams may be able to detect application performance concerns ahead of time. You’ll also obtain valuable insight into the end-user experience by seeing the entire request journey from beginning to conclusion. It enables IT teams to respond quickly to problems even though the organization is expanding its application infrastructure to accommodate future growth. 

Bring observability to everything

You can’t afford to spend months or years developing your tools or comparing other services. They only assist you in resolving one aspect of the observability problem. Instead, you’ll need a solution to help you make all of your systems and apps visible. To provide you with actionable outcomes as soon as possible and deliver technical and business value.

About Enteros

Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of RDBMS, NoSQL, and machine learning database platforms.