Why Is Observability Important for IT Teams

Observability is crucial for IT teams as it helps them understand how their systems are performing in real-time. This is especially important for modern applications, which often involve complex interactions between multiple components.

The average application has around 100-200 services, making it difficult to understand what's happening without observability. Without it, teams are left guessing and troubleshooting in the dark.

Observability provides a unified view of an application's performance, including metrics, logs, and traces. This helps teams identify issues quickly and resolve them before they become major problems.

By implementing observability, teams can reduce downtime and improve overall system reliability.

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Observability is the ability to monitor and understand the behavior of complex systems, such as software applications, infrastructure, and networks. This allows teams to identify issues, troubleshoot problems, and optimize performance.

Observability involves collecting data from multiple sources, including logs, metrics, and traces. This data is used to gain insights into how the system is functioning, identify bottlenecks, and optimize performance.

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Having observability in place helps teams to quickly identify and resolve issues, reducing downtime and improving overall system reliability. In fact, a study found that companies with good observability practices experience 50% less downtime than those without.

Observability is not just about monitoring, but also about understanding the root cause of issues and making data-driven decisions to improve the system. By doing so, teams can reduce mean time to resolve (MTTR) issues and improve overall system performance.

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Observability is critical in software development because it gives you greater control over complex systems. Simple systems have fewer moving parts, making them easier to manage.

In a distributed environment, understanding a current problem is an enormous challenge, largely because it produces more "unknown unknowns" than simpler systems. This is because monitoring requires "known unknowns", which often fails to adequately address problems in these complex environments.

Observability is better suited for the unpredictability of distributed systems, mainly because it allows you to ask questions about your system's behavior as issues arise.

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Here are the three key pillars to achieving observability:

Observability addresses the common issue of "unknown unknowns", enabling you to continuously and automatically understand new types of problems as they arise.

By providing real-time visibility into production systems, observability gives developers better visibility into their applications and infrastructure. This can help remove impediments to development and improve the overall development process.

Observability also helps developers discover and fix problems faster, providing deeper visibility that allows them to quickly determine what has changed in the system and debug or fix the issues.

Observability provides better visibility into production systems, allowing developers to know what services are in production, how application performance is, and who owns a certain service in real-time.

This visibility helps remove impediments that hinder developers' work, such as tracking down information through third-party companies and apps to find out who was responsible for a particular service or what the system looked like days or weeks before the most-recent deployment.

Observability gives developers real-time visibility, making it easier to determine what has changed in the system, debug or fix issues, and determine what problems those changes have caused.

With observability, developers can see a request's end-to-end journey, along with relevant contextualized data about a particular issue, which streamlines the investigation and debugging processes for an application, optimizing its performance.

This results in increased speed of delivery and more time for engineering staff to come up with innovative ideas to meet the business and its customers' needs.

Here are some of the key benefits of observability:

These benefits ultimately help developers get the job done faster and better, allowing them to focus on strategic initiatives that benefit the business.

Implementing observability involves a combination of instrumentation methods, including open source instrumentation tools like OpenTelemetry, to collect telemetry data from across your system. This data is then processed and correlated to create context and enable automated or custom data curation for time series visualizations.

Typically, there are four components involved in implementing observability: instrumentation, data correlation, incident response, and AIOps. Instrumentation is the process of collecting telemetry data from a container, service, application, host, and any other component of your system, enabling visibility across your entire infrastructure.

To achieve observability, you can build your own tools, use open source software, or buy a commercial observability solution. This can help you detect and analyze the significance of events to your operations, software development life cycles, application security, and end-user experiences.

Here are the four components involved in implementing observability:

To implement observability, you need to collect telemetry data from your systems and apps. This can be done by building your own tools, using open source software, or buying a commercial observability solution.

Typically, there are four components involved in implementing observability: instrumentation, data correlation, incident response, and AIOps. Instrumentation involves collecting telemetry data from containers, services, applications, hosts, and other components of your system.

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Data correlation is the process of processing and correlating telemetry data from across your system, creating context and enabling automated or custom data curation for time series visualizations.

Incident response involves getting data about outages to the right people and teams based on on-call schedules and technical skills. AIOps uses machine learning models to automatically aggregate, correlate, and prioritize incident data.

Some organizations implement observability using a combination of instrumentation methods, including open source instrumentation tools like OpenTelemetry. Others adopt an observability solution to detect and analyze the significance of events to their operations, software development life cycles, application security, and end-user experiences.

Manual instrumentation and configuration can be time-consuming and take away from innovating based on insights from observability data. This can be avoided by using a commercial observability solution or open source software that can handle instrumentation and data correlation for you.

Here are the four components of observability in a concise list:

Choosing the right observability tools is crucial to the success of your observability initiative. Make sure they support the frameworks and languages in your environment, container platform, messaging platform, and any other critical software.

Your observability tools should integrate with your current tools, otherwise, your efforts will fail. This means they should work with your existing stack.

Be user-friendly is key, if your tools are hard to learn or use, they won’t get added to workflows. This can prevent your observability initiative from getting off the ground.

Your tools should provide real-time data, so teams can understand an issue, its impact, and how to resolve it. This is crucial for timely incident response.

Effective observability tools should support modern event-handling techniques, such as collecting all relevant information from across your stacks, technologies, and operating environments.

Here are some key features to look for in an observability tool:

Implementing observability requires more than just collecting telemetry data. You need to make it actionable and scalable for IT teams.

To achieve this, teams can specify instrumentation and data aggregation in a Kubernetes cluster configuration, enabling them to gather telemetry from the moment the cluster spins up, until it spins down.

Manual instrumentation and configuration can be time-consuming and takes away from innovating based on insights from observability data.

Automating instrumentation and configuration can help teams scale their observability efforts.

Here are some key considerations for making observability actionable and scalable:

Observability has always been a challenge, but cloud complexity and rapid pace of change have made it an urgent issue.

Cloud environments generate a massive volume of telemetry data, making it difficult to keep up with the flow of information.

Organizations face challenges with observability, including a far greater variety of telemetry data than teams have ever had to interpret in the past.

Individual developers and software engineers benefit from observability because of the visibility it provides into their entire architecture.

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This enables them to more easily fix and prevent problems, and fosters a greater understanding of system performance.

Observability also allows teams to access the same insights about services, customers, and other system elements, creating a more comprehensive understanding of the environment.

This shared view of the environment helps teams understand why incidents occurred, so they can better prevent and handle future incidents.

Observability gives businesses the tools to understand what’s working and what’s not, pinpoint issues, and quickly improve or resolve them, resulting in happier customers, a better end-user experience, and a more robust bottom line.

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Resolving issues is a crucial part of any IT team's workflow. With observability tools, teams can accelerate issue discovery and resolution processes, keeping app availability high and mean time to repair (MTTR) low.

Observability solutions provide real-time system monitoring, enabling IT teams to identify and fix issues quickly. This is because observability tools provide end-to-end health and performance telemetry, allowing teams to troubleshoot issues much faster.

Causal AI is a game-changer in resolving issues. By analyzing the relationships and interdependencies between infrastructure components, causal AI helps teams pinpoint the root causes of operational and quality issues. This enables developers to understand not just the "when and where" of system issues but the "why", helping teams resolve problems faster and boosting system reliability.

Observability in containers and microservices exposes the state of applications in production, allowing developers to identify and resolve performance issues more easily. This is particularly important in cloud environments, where interdependent microservices are scattered across multiple hosts, making it difficult for DevOps teams to know what's currently running in production.

Here are some key benefits of using observability to resolve issues:

By leveraging observability tools and techniques, IT teams can resolve issues more efficiently and effectively, leading to improved app performance, reduced downtime, and increased customer satisfaction.

Data silos can be a real challenge, especially when you have multiple agents and disparate data sources. This can make it difficult to understand the interdependencies across applications.

Multiple clouds and digital channels, such as web, mobile, and IoT, can add to the complexity. It's hard to get a clear picture of what's going on when everything is fragmented.

Siloed monitoring tools can make it even harder to get a unified view of your systems. This can lead to delays and inefficiencies in your operations.

In reality, data silos can cause a lot of problems, from slow response times to missed opportunities. It's essential to break down these silos to get a clear understanding of your systems and data.

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To achieve complete observability, it's essential to use multiple tools and vendors, as a single tool may not provide comprehensive visibility across all applications and systems that impact performance.

Having the right tools in place can make a huge difference in identifying and resolving issues quickly. This is especially true when dealing with complex application architectures that involve multiple tools and vendors.

In fact, using multiple tools and vendors can help you catch issues that might have otherwise gone unnoticed, allowing you to respond faster and more effectively to performance problems.

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Using multiple tools and vendors can give you a more comprehensive view of your application's performance. However, relying on a single tool may not provide complete observability across all applications and systems that can affect performance.

One tool may only give you a partial picture of what's going on, leaving you with blind spots and making it harder to identify issues. This is because different tools specialize in different areas, such as logging, monitoring, or tracing, and may not work seamlessly together.

To get a complete view, you need multiple tools that can work together to provide a unified understanding of your application's performance. This can be especially challenging in complex environments with many different systems and applications.

Fortunately, there are tools like OpenTelemetry that can help bridge the gap between different tools and vendors. OpenTelemetry is an open-source project that provides a standard for collecting telemetry data, making it easier to integrate with multiple tools and vendors.

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Real-user monitoring can give you real-time visibility into the user experience, tracking the path of a single request and every interaction it has with every service.

Organizations can use synthetic monitoring to observe the user experience, or even view a recording of the actual session.

Teams can access real-time insight into system health, seeing the complete end-to-end journey of a request.

With real-user monitoring, IT, DevSecOps, and SRE teams can proactively troubleshoot areas of degrading health before they impact application performance.

Real-user monitoring extends telemetry by adding data for APIs, third-party services, errors occurring in the browser, user demographics, and application performance from the user's perspective.

This allows teams to recover from failures and gain a more granular understanding of the user experience.

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Observability is crucial for modern applications, and one of the key benefits is its ability to improve application availability. Advanced observability solutions can provide end-to-end distributed tracing across serverless platforms, Kubernetes environments, microservices, and open-source solutions.

This allows teams to gain visibility into the complete journey of a request from start to finish, enabling them to proactively identify application performance issues. By doing so, IT teams can quickly act on issues of concern, even as the organization scales its application infrastructure to support future growth.

With observability, teams can pinpoint root causes of issues before they result in degraded application performance or accelerate their time to recovery. This is made possible by a single source of truth, which makes it easier to interpret the vast stream of telemetry data arising from multiple sources.

Causal AI is a branch of AI that focuses on clarifying and modeling causal relationships between variables, rather than just identifying correlations.

Traditional AI techniques often rely on statistical correlation to make predictions, but Causal AI aims to find the underlying mechanisms that produce correlations.

Incorporating Causal AI into observability systems can significantly enhance organizations' insights into their IT environments.

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Causal AI enables IT teams to analyze the relationships and interdependencies between infrastructure components, so they can better pinpoint the root causes of operational and quality issues.

It empowers developers to understand not just the "when and where" of system issues but the "why", helping teams resolve problems faster and boosting system reliability.

By using Causal AI, teams can make more targeted decision-making and improve predictive power.

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Large language models (LLMs) excel at recognizing patterns in vast quantities of repetitive textual data, which closely resembles log and telemetry data in complex, dynamic systems.

Advancements in LLMs can help users of observability tools to write and explore queries in natural language, moving away from complex query languages.

LLMs aren't yet appropriate for real-time analysis and troubleshooting, because they often lack the precision to capture complete context.

This limitation means LLMs are best used to simplify data insights in observability platforms, making it easier to understand system behavior and IT issues.

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In the tech industry, observability is crucial for detecting and resolving issues quickly. This is because complex systems can be difficult to monitor and diagnose.

The cost of downtime can be staggering, with some companies losing up to $5,000 per minute. This is why observability is essential for businesses that rely on high-performance systems.

With observability, developers can gain a deeper understanding of their systems and make data-driven decisions to improve performance and reduce errors.

Splunk is a leader in the industry when it comes to observability, a concept that's been around for decades and is now widely adopted by organizations. According to the State of Observability, as many as 87% of organizations now employ specialists who work exclusively on observability.

Splunk is recognized as a Leader in Observability and Application Performance Monitoring by Gartner. You can view the Gartner Magic Quadrant to find out why.

Observability uses three types of telemetry data – metrics, logs, and traces – to provide deep visibility into distributed systems. This allows teams to get to the root cause of issues and improve system performance.

Splunk Observability Cloud offers a free trial and demo video to learn more about its products and solutions.

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Foster an open ecosystem by leveraging open source solutions like OpenTelemetry, which is an open-source project led by vendors such as Dynatrace, Google, and Microsoft.

OpenTelemetry expands telemetry collection and ingestion for platforms that provide topology mapping, automated discovery and instrumentation, and actionable answers required for observability at scale.

By incorporating OpenTelemetry, organizations can collect telemetry data more effectively, making it easier for developers and operations teams to achieve a consistent understanding of application health across multiple environments.

This open ecosystem approach extends observability to include external data sources, providing a more comprehensive view of the environment.

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Monitoring cloud and Kubernetes environments can be a challenge, but with the right tools, you can improve application uptime and performance. Infrastructure and operations teams can leverage observability solutions to monitor on-premises and cloud infrastructure.

A unified observability-based approach can cut down the time required to pinpoint and resolve issues. This is especially true for cloud latency issues, which can be difficult to detect without the right tools.

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Observability solutions can also help optimize cloud resource utilization. By monitoring cloud infrastructure, teams can identify areas where resources are being wasted and make adjustments to improve efficiency.

Detecting cloud latency issues is crucial for maintaining application performance. Without observability solutions, teams may struggle to identify the root cause of latency issues, leading to frustrated users and lost productivity.

Modern cloud architectures can be complex, but observability solutions can help simplify administration. By providing a unified view of cloud infrastructure and Kubernetes environments, teams can more easily manage and troubleshoot issues.

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