The practice of DevOps — development operations — has taken organizations by storm. According to a 2021 report by Redgate Software, 74 percent of enterprises surveyed say they now use DevOps in some form or fashion, compared with just 47 percent in 2016.

DevOps practitioners seek to improve the software development lifecycle by fostering closer collaboration between developers and IT operations teams. Formally, DevOps can be defined as a collection of different practices, techniques, and methodologies to improve software quality and delivery speed, such as automation and CI/CD (continuous integration/continuous delivery).

The field of DevOps includes a number of novel ideas and terminologies that may be unfamiliar to an organization. Observability and monitoring are two such DevOps concepts that are strongly linked and sometimes even used synonymously.

Here’s what you need to know about the differences between observability and monitoring:

  • Observability enables users to understand an IT system’s health and status by providing inputs and getting feedback.

  • Monitoring involves the collection of data about the performance of an IT system.

  • Observability is a property of an IT environment, while monitoring is an action taken within that environment.

  • Monitoring is often not sufficient to understand a complex IT environment in its entirety.

  • Observability should be able to detect unanticipated events outside the limited framework of monitoring.

Although there is a significant correlation between observability and monitoring, however, the two terms don’t describe precisely the same thing. So what are the key differences between observability and monitoring, and what impact do these differences have for DevOps teams? Keep reading for the answer to these questions and more.

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What Is Observability?

In the field of DevOps, “observability” refers to the ability of IT teams to understand the status and health of an IT ecosystem based on the external outputs it produces.

The abstract concept of observability has its origins in control theory: a branch of engineering and mathematics that studies the behavior of dynamic systems. By providing different inputs to a system and observing the desired output, individuals can gain valuable feedback about which actions are most valuable to achieve a desired goal and optimize their behaviors as a result.

With DevOps, the observable system is the IT environment itself, which provides real-time output in the form of various logs and metrics (as we’ll discuss below). DevOps teams can use these outputs to better comprehend the system’s inner workings and take corrective action if necessary, delivering a better end-user experience.

DevOps practitioners have defined the “three pillars of observability,” which describe the three most essential sources of information about a system’s internal states. These three pillars are:

  1. Metrics: Metrics and key performance indicators (KPIs) are measurable, numerical values that assess how well an organization is accomplishing a particular objective. To diagnose a system’s health, for example, the set of metrics might include the amount of latency when responding to requests or how much downtime it had in the past month. Metrics are important because they provide teams with a way to compare performance over time, as well as a method of detecting anomalies and aberrations from the norm.

  2. Logs: Logs are lists or documents that extensively catalog the activities and operations of a particular server, computer, or software application. Consulting the contents of a log helps DevOps teams understand how different elements in an IT ecosystem are performing before, during, and after deployment. Logs can also be used to perform root cause analysis (RCA) for troubleshooting and debugging the source of an error or performance issue.

  3. Traces: The concept of a trace extends the notion of a log to distributed systems, such as in a multi-cloud or microservices architecture with many different interacting parts. Because understanding events in these distributed systems is by nature more complicated, traces are often displayed in a visual format, showing how an operation moved from one node to another in the system.

To support the goal of observability, many businesses practicing DevOps make use of dedicated observability tools. For example, OpenTelemetry is an open-source observability framework that includes different tools, APIs (application programming interfaces), and SDKs (software development kits) for examining software performance and behavior. This is one example of instrumentation: using an observability platform to collect actionable data — just as doctors use instruments like stethoscopes and blood pressure monitors to collect data on the health of a patient.

What Is Data Observability?

Other domains, too, can benefit from applying this concept of observability. For example, DataOps (data operations) seeks to adopt the practices and technologies of DevOps within the field of data management.

The goal of DataOps is to encourage greater collaboration between the data engineers and data scientists who build analytics pipelines and the business users who interpret the final outputs. In DataOps, data teams define data pipelines that aggregate multiple sources and inputs, perform the necessary data transformations, and load the transformed data into a centralized repository (such as a data warehouse). Here, the information can be converted into user-friendly dashboards, visualizations, and reports for better decision-making.

DataOps practitioners have used the general concept of observability to formulate a more domain-specific notion: data observability. Whereas observability is concerned with the general health of the entire IT ecosystem, data observability applies specifically to the status and health of an organization’s data (e.g., in terms of data quality and availability).

To support this notion, DataOps teams have developed the idea of the “five pillars of data observability,” echoing the “three pillars of observability” discussed above. In no particular order, these are:

  • Freshness: How “fresh” or up-to-date a company’s information is. Data freshness is essential for making the most accurate forecasts and smartest decisions.

  • Distribution: Whether an organization’s data is in accordance with expectations (e.g., whether it falls within an expected range). Distribution anomalies could indicate underlying errors or performance issues that need to be corrected.

  • Volume: The amount of data that a business has access to. Data volumes should generally remain constant or increase over time unless the organization is performing data cleansing (e.g., removing inaccurate, out-of-date, or duplicate data sets).

  • Schema: The design or structure of a database or table, describing how information is stored within it. Any changes to a database schema must be carefully executed in order to avoid breaking the database and making its contents inaccessible.

  • Lineage: The “big picture” of an organization’s data landscape, showing how different data assets and dependencies are connected. Untangling this convoluted network is essential to understand how data flows throughout the business and to detect and resolve any data quality issues.

By following these five pillars of data observability, data teams can dramatically increase the quality of their pipeline’s final output in terms of high-quality, actionable insights for business intelligence and analytics.

What Is Monitoring?

In the field of DevOps, “monitoring” refers to collecting data about the performance of a given IT environment, system, or software app. When it specifically pertains to software, this data collection is referred to as APM (application performance monitoring). The results of monitoring system performance can then be processed, analyzed, and mined for valuable IT and business insights.

Nearly any kind of IT asset can be monitored along a wide range of axes. A computer’s CPU (central processing unit), for example, can be monitored in terms of its internal temperature, utilization, maximum speed, idle time, and more. A cluster of Kubernetes containers, meanwhile, can be monitored on multiple levels: both at the cluster-wide level (e.g., the number of available nodes) and the pod level (e.g., the resource utilization within a specific pod).

Profiling is a special form of monitoring that focuses on application performance as it applies to software development. For example, developers may use profiling to see which parts of an application are run the most frequently or which parts consume the most CPU time. In turn, this information can be used to optimize the relevant sections of the application’s code base (e.g., by replacing a data structure with a more efficient alternative).

To assist in the act of monitoring, companies typically use dedicated monitoring tools. Companies such as SolarWinds are market leaders in the fields of network and application monitoring, while Zabbix and Nagios are solid open-source alternatives.

Monitoring often takes a long-term approach, homing in on a particular metric or KPI and tracking its values over time. The information gleaned from monitoring can be used in dashboards and visualizations to help business users see the state of important metrics at a glance within a single pane of glass.

Observability vs. Monitoring: 3 Differences to Know

Now that we’ve provided comprehensive definitions of both observability and monitoring, it’s time to get down to brass tacks. Below, we’ll go over three of the most important differences between observability and monitoring.

Observability vs. Monitoring Difference #1: Have vs. Do

Perhaps the most obvious way to think about the difference between observability and monitoring is the basic definition of these two terms.

On the one hand, observability is a property of a given IT ecosystem. If we say that an IT environment (or a component of that environment) is “observable,” this means that we can know its inner workings based on the output that it produces. Having an IT environment with high observability is a desirable goal because it allows us to understand the environment on a deeper level and quickly diagnose any problems.

On the other hand, monitoring is something that IT teams do within that ecosystem. In other words, monitoring is not a property of an IT environment but an action that can be done to that environment.

Depending on your philosophical stance, you might say that observability is a necessary property before monitoring can be done (i.e., a system must be observable in order to be monitored). You might also say that monitoring enables observability, i.e., a system becomes observable through techniques such as monitoring.

This is a bit like the “chicken or the egg” problem, so there’s no clear answer — but what is clear is that observability and monitoring aren’t the same thing.

Observability vs. Monitoring Difference #2: Scope

The second difference between observability and monitoring is in terms of their scope or objectives.

With monitoring, the goal is simply to collect data about the operations of a particular system or software application, particularly as this data changes over time. Monitoring is usually highly specific, concentrated on a few select metrics and KPIs that users have deemed most important. For the simplest of IT systems, monitoring is often sufficient to understand the system’s health and status in its entirety.

However, for more complicated environments (such as distributed systems), monitoring certain metrics alone is usually not enough to get a full picture of the holistic state of the IT ecosystem. These environments are too complex to be summed up by a few figures on a screen, with many interlinking nodes and long chains of causation between a root cause and its observable symptoms.

To achieve true observability for large-scale IT environments, monitoring can only be one piece of the puzzle. Observability should draw on monitoring as well as other techniques such as log analysis, system and relationship modeling, and machine learning.

Observability vs. Monitoring Difference #3: Predictability

Last but not least, one crucial difference between observability and monitoring is that of predictability.

Monitoring necessarily involves observing certain predefined metrics and KPIs about a system or application. As such, monitoring may only be able to identify problems, issues, and anomalies within the framework that the DevOps team has already anticipated. Any unpredictable phenomena that arise in the course of monitoring may thus go undetected.

Observability, on the other hand, involves understanding the entire system on a fundamental level, including how providing a given input will lead to a given output. An organization that has achieved true observability is one that can successfully detect and respond to all events and feedback, both foreseen and unanticipated.

How Can Help With Observability and Monitoring

While they’re closely related, observability and monitoring are distinct concepts that need to be treated as such. What is clear, however, is that businesses must endeavor to achieve both observability and monitoring in order to better understand their IT environments.

By collecting information from different sources and storing it in a single repository, data integration is a fundamental practice for data-driven businesses. As such, DevOps-focused organizations must select the platform that best fits their needs from the right data integration provider. is a powerful, cloud-based, feature-rich yet user-friendly ETL (extract, transform, load) and data integration tool that can be deployed both in the cloud and on-premises.

Purpose-built specifically for the needs of Ecommerce businesses, helps users of all backgrounds and skill levels improve the performance and quality of their data integration processes. Thanks to’s more than 140 pre-built connectors and its no-code, drag-and-drop interface, even non-technical business teams can effectively use the platform to define their own robust, production-ready data pipelines. includes advanced features and functionality to support a wide variety of data integration use cases. With the FlyData CDC (change data capture) feature, for example, users can easily identify precisely which data records and tables have changed since their most recent integration job. This allows users to save countless hours of time and effort by only ingesting the data they need.

Reverse ETL is another feature that lets users move information out of a centralized data warehouse and into third-party systems. By migrating this data, reverse ETL makes it more accessible to users who lack the in-depth technical knowledge necessary to work directly within the data warehouse.

Want to see for yourself how can dramatically improve your data integration workflow? Get in touch with our team of data experts today for a chat about your business needs and objectives or to start a 14-day pilot of the platform.