Observability vs. Monitoring vs. Telemetry: Understanding the Differences and How to Use Them Effectively
Every day, Software and Site Reliability Engineers, along with IT Managers, encounter daunting challenges to building responsive and reliable production environments.
Unfortunately, increased levels of IT complexity very often come with a lack of visibility into modern software development and operations environments. More visibility is needed to detect performance issues before they become problems, and multiple approaches are evolving that incorporates a vast array of complex performance solutions and tools.
The advent of DevOps principles, project management approaches, and practical implementation track record is entwined with observability, monitoring, and telemetry practices. DevOps and Agile practices should be fundamental in devising an overall strategy for integrating observability, monitoring, and telemetry functions and tools at every stage of deployment and deployment cycles.
Working practices, resources, skills, and tools aimed at improving overall performance and visibility are being leveraged to:
- Achieve continuous deployment of high-quality products and services
- Visualize comprehensive infrastructures over complete life cycles
- Cross-organizational silos and close gaps in isolated methodologies and tools
- Enhance identification and diagnosis of problems
- Improve the end-user experience
- Integrate performance data across cloud and distributed environments, tools, and performance data types
This review of principles and practices related to observability, monitoring, and telemetry is intended to enrich the understanding of these practices as well as to describe approaches that can combine and integrate best practices and technologies.
Overview of Observability, Monitoring, and Telemetry
While principles and practices of observability, monitoring, and telemetry overlap to some extent, there are differences in goals and uses:
- A solid grasp of observability practices is needed to support proactive tactics and build broader knowledge about how IT infrastructures operate. Greater observability results in valuable insights from data collected during monitoring and telemetry processes. This capability leads to more intelligent and robust troubleshooting, correlation across diverse system and user issues, and discovery of new anomalies and patterns.
- Monitoring practices collect and analyze predetermined data obtained from individual systems via the telemetry function, detect problems, and ensure corrective action. Compared to observability, monitoring has been considered an operational function providing event notifications without uncovering many possible other factors. However, the current widely discussed benefits of observability are driving attention toward enhancing monitoring capabilities and toolsets.
- Telemetry makes data collection and use possible through observability and monitoring functions. Attention is focused on streamlining performance processes by automating telemetry processes and leveraging knowledge gained about user engagement and technical application characteristics.
Observability has been defined as the capability to visualize and infer internal system states based on knowledge of system outputs.
When this capability is applied to infrastructure performance, it refers to a combination of principles, goals, methodologies, and tools that enable IT teams to use advanced techniques to understand multiple environments and technology issues. The end goal is to facilitate rapid, proactive detection and resolution of performance issues, including uptime and reliability.
New approaches are leveraging the ability of observability practices and tools to gain visibility at the infrastructure component level. This capability makes it possible to capture and appreciate the quality and relevance of data collected as a part of the telemetry, monitoring, analysis, and remediation cycle.
The wide scope of observability also enables insights across comprehensive IT environments to help troubleshoot root causes.
Software tools and methodologies addressing observability capabilities can allow teams to log, collect, correlate, and analyze larger amounts and varieties of performance data and to produce near real-time insights.
Ultimately, organizations can deploy the results achieved to monitor, enhance and re-architect applications and infrastructures to deliver better customer experience, accuracy, and faster delivery.
Monitoring practices and tools can prevent disruptions and outages and collect data from a wide variety of sources. This capability helps detect IT infrastructure issues and safeguards infrastructure availability and performance levels.
After health and performance data has been collected from a wide range of hardware, software, and network components, monitoring tools aggregate data to be sorted, queried, and analyzed by computer applications and humans. Operational and performance data to diagnose and correct issues are then presented via KPI dashboards and charts.
Implementing IT Monitoring
Many topics need to be considered when implementing IT system monitoring. An initial step is to break down the IT environment into systems and events that need to be monitored and to define metrics and associated alerts. Some situations are at a higher risk and require increased focus, including:
- System updates (risk of failure or unintended errors)
- Application deployments and rollbacks
- Peak transaction times
One of the biggest challenges of implementing monitoring in complex systems is the “Too Big to Monitor” issue: the exploding volume of devices, networks, vendor tools, cybersecurity threats, costs, evaluation of new technologies, and inefficiencies of legacy monitoring tools.
All of these challenges need to be met with an emphasis on long-term strategic planning to enable feasible migrations and the knowledgeable workforce needed.
The telemetry function collects data for a very large number of data points/devices at the same time in complex systems. Telemetry supports remote monitoring of the health, security, and performance of infrastructure components in real-time. Telemetry makes it possible to collect the raw data needed for monitoring and observability functions along with actionable analytics.
Telemetry solutions are expanding around the use of new inexpensive devices and the expansion of the Internet of Things. The OpenTelemetry project aims to develop a standardized telemetry approach for applications that use telemetry to understand the performance and behavior of distributed systems. Remote device data is unstructured, and the number of data points and frequency of data collection are expanding. This means that finding useful data integration techniques for telemetry data is needed.
Performance data is acquired using automated collection, transmitted from remote sources, and sent to centralized infrastructure platforms for monitoring and analysis. Telemetry implementation for application monitoring, for example, encompasses four steps:
- Analysts and developers work with users to specify metrics
- Plans are put in place to ensure transmission data quality and safety for the user’s activity
- Techniques are used to make data simpler for analysis
- Data is analyzed to assess the performance of the component
The monitoring function uses telemetry data collected to communicate if any metrics fall outside the specified threshold. Telemetry data types include metrics, events, logs, and traces.
Benefits of Telemetric Analysis
Once an application is deployed, telemetric analysis helps developers to target the best software features based on the frequency of use and other factors. Based on this type of analysis, developers can improve features.
Moreover, tools can keep track of performance from all users across geographic locations. Developers can also learn the most common screen configuration and display backgrounds and feed this knowledge into application upgrades. Telemetry analysis is also important in its ability to report on metrics related to user engagement.
Integration Across Performance Data
Metrics, logs, and traces are basic categories of performance data and are key to integrating across observability, monitoring, and telemetry functions in complex dynamic systems.
Each category of data goes only so far into seeing the complete picture needed to meet the demand for visibility across multiple infrastructures, applications, and toolsets. The ability to derive insights based on data from all three categories provides engineers, developers, and operators with the robust foundation needed to move toward integrated data and the benefits of advanced analytics and tools. Performance data and applications leverage the metrics, traces, and logs data, as described in the following sections.
Logs, made up of lines of text produced by an application or service at given stages, are the most basic data type. They are timestamped and record basic information about system performance. They may be generated, for example, to indicate an error, such as a query that has taken too long. This information enables tracking when an event happens and allows correlation of associated events.
There is a substantial amount of support for logging from a wide var of programming languages, application frameworks, and libraries, but it is hard to analyze this information to obtain insights, and the large data stores can be expensive. In addition, log text can be lost, and their scope is small compared to the needs of advanced visibility requirements.
Metrics are related to the behavior and characteristics of a system and can be aggregated or measured over a period of time. Functions of collecting and analyzing metrics include measuring performance, providing data for alerts such as when systems are unavailable, and being used in monitoring events for performance issues.
Examples of metrics include CPU capacity, memory usage, error rates, response time, and peak load. Metrics, like logs, are often limited to single systems’ performance.
Advanced platforms would be able to combine and compare different types of data in real-time to provide results that show visibility performance issues. This integrated view analysis of logs, metrics, and traces is essential. In legacy situations where multiple tools are used to collect performance data, gaps, and overlaps, problems with identifying issues frequently occur.
Identifying and selecting integrated platforms and approaches that can look across multiple diverse sets of data are important steps. Looking for additional sources of data such as API, third-party feedback, demographic, and user feedback can also give perspective.
Traces give information about the journeys of user or system actions moving through the components of distributed environments. In addition, traces provide more context than logs or metrics, making this data type essential for monitoring complex infrastructures.
The amount of data to support tracing is small as compared to logs, and it is used to identify performance issues and bottlenecks. By viewing traces, developers can understand cause-and-effect relationships, measure time spans for key actions to be performed, and by clarifying where errors occurred, contribute to faster problem resolutions.
Increased levels of IT complexity come with a lack of visibility into modern software development and operations environments. This rise of IT infrastructure complexity is driving interest in performance solutions related to observability, monitoring, and telemetry approaches.
There is broad support for the idea that observability can have a much broader scope and reach than monitoring when fully implemented and that it can incorporate advanced functions related to AI and data analytics.
Telemetry, concerned with the transmission of data from remote sources, is essential for both observability and monitoring. Leveraging streamlined DevOps practices and harmonizing multiple performance tools are effective ways to create holistic views of health and status over the entire scope of complex architectures.
Learn how providing wide visibility, monitoring, and telemetry options and tools can meet performance challenges and toolsets of today and the future by contacting xxxx.
How does telemetry differ from observability and monitoring?
Telemetry provides automated processes to obtain data needed for analysis for both observability and monitoring functions. This data includes measurement data needed by observability and monitoring tools. This data is essential to enable monitoring to detect issues and correct them. In addition, this data provides some of the data needed by the observability function to produce analytics and determine root causes.
What are some common challenges of implementing observability, monitoring, and telemetry?
- The complexity of modern IT infrastructures results in fractured solutions, overlapping tools, lack of visibility into unanticipated patterns and issues
- The need to assess new tools and integration requirements to fit the performance needs of complex technical environments
- The culture of siloed teams that may not want to move to a wider scope and innovative solutions
- The need to nourish C-suite support for innovative performance improvements
What tools are available for implementing observability, monitoring, and telemetry?
- Tools that provide the foundation for observability include comprehensive support of telemetry across diverse infrastructures, applications, and end users.
- Tools that provide a standard workflow to build context and determine priorities.
- Observability and monitoring tools that work together to collect telemetry data, monitor, and provide insight into the overall IT infrastructure.
How can I optimize my observability, monitoring, and telemetry setup?
- Survey trends and new technology products incorporating advanced performance tools
- Review tools and procedures to establish a baseline and resources and capabilities available
- Analyze customer experience—for example, if the system is down or provides a bad experience
- Review and enhance monitoring systems metrics
- Review and enhance tools for systems in production
- Survey and evaluate tools that can find issues not previously known
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