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[...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/en/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"]}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"section_0":{"columnClassNames":{"text_0":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"text_0":{"id":"text-dc98e8c97b","text":"\u003Cp\u003EObservability vs. monitoring is a common comparison for teams running modern cloud-native and distributed systems. The terms are often used interchangeably, but these two approaches answer very different questions. While monitoring tells you when something is wrong in your system, \u003Ca href=\"https://www.snowflake.com/en/fundamentals/observability/\"\u003Eobservability\u003C/a\u003E helps you understand why. As systems grow more complex and distributed, this distinction is increasingly important. This guide explains the differences between the two approaches, compares their technical capabilities and helps you decide which approach best fits your organization’s needs.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_0"]},"section_1":{"columnClassNames":{"text_1":"aem-GridColumn aem-GridColumn--default--12","text_0":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","h3_title_0":"aem-GridColumn aem-GridColumn--default--12","text_2":"aem-GridColumn aem-GridColumn--default--12","h3_title_1":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"what-is-the-difference-between-observability-and-monitoring",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-01a3b11390","type":"heading2","lines":["What is the difference between observability and monitoring?"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-8968408550","text":"\u003Cp\u003EMonitoring is a traditional approach to system oversight. It allows you to track predefined metrics against known thresholds. For example, when CPU use exceeds 90% or response times dramatically increase, your system sends out an alert. Monitoring is primarily reactive and based on predefined signals — the system is on the lookout for failures that have been defined in advance. This approach works well in stable, predictive environments.\u003C/p\u003E\r\n\u003Cp\u003EIn contrast, observability (a term adopted from \u003Ca href=\"https://www.britannica.com/science/control-theory-mathematics\"\u003Econtrol theory\u003C/a\u003E) is the ability to infer a system’s internal state from its external outputs. An observable system is one that produces rich telemetry to help answer not just the question “what is happening?” but “\u003Ci\u003Ewhy\u003C/i\u003E is it happening?” In cloud environments, this becomes even more critical — see how \u003Ca href=\"https://www.snowflake.com/en/fundamentals/observability/cloud/\"\u003Ecloud observability\u003C/a\u003E enables real-time visibility across distributed systems.\u003C/p\u003E\r\n\u003Cp\u003ESystem predictability has declined over the years as organizations have adopted newer technologies such as \u003Ca href=\"https://www.snowflake.com/en/fundamentals/microservices/\"\u003Emicroservices\u003C/a\u003E, cloud-native infrastructure and distributed architectures. This means that software and data systems now often fail in ways no one predicts, and traditional monitoring can’t always explain what went wrong. This has led to the growing importance of observability in the modern enterprise.\u003C/p\u003E\r\n\u003Cp\u003EMonitoring is one of the primary use cases enabled by observability — rather than replacing monitoring, observability expands on it by providing the deeper context needed to understand and investigate issues.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-b123bd2bf1","type":"heading3","lines":["Understanding modern observability"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-41dbc5f03e","text":"\u003Cp\u003EObservability correlates three primary data types, often described as \u003Cb\u003Emetrics, logs and traces\u003C/b\u003E (sometimes expanded to include events, profiles and other telemetry). Metrics offer a high-level view of system health, logs capture discrete events, and traces follow a single request from end to end to reveal where time was spent and failures occurred. The ability to correlate all three of these data types — in real time across a distributed system — is what distinguishes observability from traditional monitoring. This foundation is especially important in data systems — see how \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-observability/\"\u003Edata observability\u003C/a\u003E helps teams ensure data reliability across modern data stacks.\u003C/p\u003E\r\n\u003Cp\u003EThis ability to correlate metrics, logs and traces allows teams to ask ad hoc questions of their telemetry data without redeploying instrumentation if the relevant dimensions were already captured, which is a valuable capability in distributed and microservices environments where incidents do not always follow predictable patterns.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-0fbd2de417","type":"heading3","lines":["Key conceptual differences"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-392aa054fe","text":"\u003Cp\u003EThe two approaches differ in philosophy as well as technology. Monitoring focuses on vigilance over what you already understand, while observability helps you develop insight into behavior you don’t yet understand.\u003C/p\u003E\u003Cp\u003EThe table below highlights key differences in how the two approaches collect data, surface problems and support investigation.\u003C/p\u003E\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EDimension\u003C/th\u003E\u003Cth\u003EMonitoring\u003C/th\u003E\u003Cth\u003EObservability\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EKnown vs. unknown\u003C/td\u003E\u003Ctd\u003EAddresses anticipated failures and known conditions\u003C/td\u003E\u003Ctd\u003EDesigned to surface unknown unknowns that nobody anticipated\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EData model\u003C/td\u003E\u003Ctd\u003ERelies on a fixed set of predefined metrics\u003C/td\u003E\u003Ctd\u003ECaptures high-cardinality telemetry that can be sliced and filtered in many dimensions\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EQuerying\u003C/td\u003E\u003Ctd\u003EUses static dashboards and alerts built around known signals\u003C/td\u003E\u003Ctd\u003ESupports exploratory querying to discover what you didn’t know to look for\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EImplementation effort\u003C/td\u003E\u003Ctd\u003ERelatively lightweight to set up\u003C/td\u003E\u003Ctd\u003ERequires greater investment in instrumentation, storage and analysis tooling\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0","h3_title_0","text_1","h3_title_1","text_2"]},"section_2":{"columnClassNames":{"text_1":"aem-GridColumn aem-GridColumn--default--12","text_0":"aem-GridColumn aem-GridColumn--default--12","text_3":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","h3_title_0":"aem-GridColumn aem-GridColumn--default--12","text_2":"aem-GridColumn aem-GridColumn--default--12","h3_title_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"observability-vs-monitoring-technical-differences",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-57214a3c71","type":"heading2","lines":["Observability vs. monitoring: Technical differences"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-6780bad7c6","text":"\u003Cp\u003EMonitoring tools collect data on a regular basis, polling systems at scheduled intervals and comparing results to configured thresholds. In contrast, observability platforms continuously capture high-cardinality telemetry data, preserving the detailed state information that enables deeper analysis when needed.\u003C/p\u003E\u003Cp\u003EThese architectural differences lead to several distinct capabilities in how monitoring and observability platforms collect, analyze and retain telemetry data.\u003C/p\u003E\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003ECapability\u003C/th\u003E\u003Cth\u003EMonitoring\u003C/th\u003E\u003Cth\u003EObservability\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EData granularity\u003C/td\u003E\u003Ctd\u003EAggregated metrics\u003C/td\u003E\u003Ctd\u003ERaw event data\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EQuery flexibility\u003C/td\u003E\u003Ctd\u003EPre-built dashboards\u003C/td\u003E\u003Ctd\u003EAd hoc exploration\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003ECorrelation\u003C/td\u003E\u003Ctd\u003ELimited to defined metrics\u003C/td\u003E\u003Ctd\u003ECross-system correlation\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003ECardinality\u003C/td\u003E\u003Ctd\u003ELow to medium\u003C/td\u003E\u003Ctd\u003EHigh\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EData retention & historical analysis\u003C/td\u003E\u003Ctd\u003ETypically 30-90 days\u003C/td\u003E\u003Ctd\u003EExtended retention possible\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-60af66ac31","type":"heading3","lines":["Analysis capabilities"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-fba79a0313","text":"\u003Cp\u003ETraditional monitoring aggregates the data it collects into metrics such as average response times or error rates per minute, and typically presents these metrics to users through fixed dashboards. While this is useful for spotting general trends, aggregated metrics lack the detail needed to diagnose unexpected incidents.\u003C/p\u003E\u003Cp\u003EObservability platforms take a different approach. Instead of relying on predefined dashboards, they provide flexible query systems that allow engineers to explore raw event-level telemetry data and ask new questions as issues arise.\u003C/p\u003E\u003Cp\u003EThese tools work with raw event-level data that can be filtered by virtually any attribute: a specific user ID, a geographic region, or a combination of request parameters. Engineers can correlate telemetry across systems, linking a log entry to the trace that generated it and the metric spike it coincided with. This type of cross-system analysis is difficult or limited with traditional monitoring tools.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-23f462b7a8","type":"heading3","lines":["Root cause investigation"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-502393e09f","text":"\u003Cp\u003EWhen a monitoring alert fires, it tells you a threshold was crossed. Engineers must use disconnected tools to then manually reconstruct events, a process that can often take hours.\u003C/p\u003E\u003Cp\u003EObservability can be powerful for diagnosing complex incidents that were never anticipated. With distributed tracing, a request is followed through every service it touches, and each step annotated with timing data and \u003Ca href=\"https://www.snowflake.com/en/fundamentals/metadata-management/\"\u003Econtextual metadata\u003C/a\u003E. This enables engineers to quickly pinpoint a specific failing operation when an incident occurs. This can help accelerate mean time to resolution (MTTR), which is often cited as a key benefit of observability platforms.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-08a2f88bda","type":"heading3","lines":["System understanding"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-98d2ded2f1","text":"\u003Cp\u003EObservability can provide capabilities beyond what monitoring alone typically offers: a living map of how systems actually behave. Service dependency mapping, dynamic baseline establishment, and automated topology discovery allow teams to see beyond outdated architecture diagrams and understand real-world behavior instead. Full-stack visibility can give operations teams the context they need to prioritize and resolve issues as well as anticipate failures before they become incidents.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0","h3_title_0","text_1","h3_title_1","text_2","h3_title_2","text_3"]},"section_3":{"columnClassNames":{"text_1":"aem-GridColumn aem-GridColumn--default--12","text_0":"aem-GridColumn aem-GridColumn--default--12","text_3":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","h3_title_0":"aem-GridColumn aem-GridColumn--default--12","text_2":"aem-GridColumn aem-GridColumn--default--12","h3_title_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"observability-vs-monitoring-use-cases",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-ce5a2c6a22","type":"heading2","lines":["Observability vs. monitoring use cases"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-f4d12f37c9","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDespite observability’s growing importance, monitoring is not obsolete — it remains the appropriate choice for environments with predictable failure modes and well-understood system behaviors. Monitoring is typically sufficient when:\u003C/p\u003E\u003Cul style=\"margin-top:0.75rem\"\u003E\u003Cli\u003ESystem components have clear, binary states (up/down, available/unavailable)\u003C/li\u003E\u003Cli\u003EPerformance thresholds are well defined and static\u003C/li\u003E\u003Cli\u003EThe infrastructure is relatively stable with infrequent changes\u003C/li\u003E\u003Cli\u003ERoot cause analysis typically follows known patterns\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003EFor example, basic web application hosting often fits this model, where key metrics such as server CPU usage, memory utilization and response times follow predictable patterns. When these metrics exceed predetermined thresholds (e.g., 85% CPU utilization, response times \u003E2 seconds), traditional monitoring can effectively trigger alerts and guide remediation.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-b067b65b28","type":"heading3","lines":["When observability becomes essential"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-a35692634f","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EThe calculus changes with complexity, however. Observability becomes crucial in dynamic, complex environments with high-cardinality data and unpredictable system behavior.\u003C/p\u003E\u003Cp\u003EKey indicators that your system requires observability include:\u003C/p\u003E\u003Cul style=\"margin-top:0.75rem\"\u003E\u003Cli\u003EMicroservices architectures with 50+ services\u003C/li\u003E\u003Cli\u003EDynamic scaling where infrastructure changes frequently\u003C/li\u003E\u003Cli\u003EComplex dependency chains spanning multiple technologies\u003C/li\u003E\u003Cli\u003EUnpredictable user behavior patterns requiring deep analysis.\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003EOrganizations may be able to reduce MTTR compared to traditional monitoring alone, depending on implementation and system complexity. This is especially important in scenarios where customer experience directly impacts revenue.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-af79d91aee","type":"heading3","lines":["AI and machine learning systems"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-02a5862289","text":"\u003Cp\u003EAI-powered applications expose a limitation of traditional monitoring: models often degrade gradually, producing subtle errors that threshold-based alerts may miss. Monitoring tracks predefined metrics such as latency and error rates, but AI systems require deeper visibility into model behavior, data quality and prediction accuracy. \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-observability/\"\u003EAI observability\u003C/a\u003E extends beyond logs, metrics and traces to help teams detect drift and trace where results begin diverging from expectations.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-324c6b282a","type":"heading3","lines":["Industry-specific requirements"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-adb90f0418","text":"\u003Cp\u003EDifferent industries face unique challenges that can influence the monitoring versus observability decision. For example, observability plays a critical role in high-stakes environments such as ecommerce during peak season or real-time financial transaction processing, where even brief degradations in performance can have serious ramifications.\u003C/p\u003E\u003Cp\u003EThe same applies to \u003Ca href=\"https://www.snowflake.com/en/blog/telecom-observability/\"\u003Etelecom observability\u003C/a\u003E, where growing network complexity makes comprehensive visibility essential for maintaining service quality and meeting regulatory requirements. In both the heavily regulated healthcare and financial services industries, detailed audit trails and the ability to reconstruct incidents precisely are necessary — something more easily supported with observability practices.\u003C/p\u003E\u003Cp\u003EThe right approach depends on an assessment of the regulatory environment in which your organization operates, along with your operational risk tolerance and the potential business impact of any system downtime.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0","h3_title_0","text_1","h3_title_1","text_2","h3_title_2","text_3"]},"section_4":{"columnClassNames":{"text_1":"aem-GridColumn aem-GridColumn--default--12","text_0":"aem-GridColumn aem-GridColumn--default--12","text_3":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","h3_title_0":"aem-GridColumn aem-GridColumn--default--12","text_2":"aem-GridColumn aem-GridColumn--default--12","h3_title_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"implementation-considerations",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-a1c4c52778","type":"heading2","lines":["Implementation considerations"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-56c5696a90","text":"\u003Cp\u003ETransitioning to observability requires more than just adopting new tooling. It also involves new professional skills, increased infrastructure demands and important cost considerations.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-7330cdc6e9","type":"heading3","lines":["Team skill requirements"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-abdf733d08","text":"\u003Cp\u003EThere is a significant skills gap between monitoring and observability teams. While monitoring teams require expertise mostly in metrics configuration and alert management, observability teams require broader capabilities:\u003C/p\u003E\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003ESkill Area\u003C/th\u003E\u003Cth\u003EMonitoring\u003C/th\u003E\u003Cth\u003EObservability\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EData analysis\u003C/td\u003E\u003Ctd\u003EBasic metric interpretation\u003C/td\u003E\u003Ctd\u003EAdvanced statistical analysis\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EProgramming\u003C/td\u003E\u003Ctd\u003EBasic scripting\u003C/td\u003E\u003Ctd\u003EDistributed systems expertise\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EQuery languages\u003C/td\u003E\u003Ctd\u003ESimple metric queries\u003C/td\u003E\u003Ctd\u003EComplex correlation queries\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EArchitecture\u003C/td\u003E\u003Ctd\u003EComponent-level understanding\u003C/td\u003E\u003Ctd\u003EFull-stack system knowledge\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\u003Cp\u003EIn addition to team members’ individual skills, your organization requires operational maturity to support observability practices. This includes clear standards for what telemetry services should emit, shared conventions for metadata and strong cross-team collaboration to investigate incidents that may occur across service boundaries.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-8cdf0d8a5b","type":"heading3","lines":["Infrastructure impact"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-2ead690cc3","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EBecause observability generates substantially more data than monitoring, it requires more sophisticated infrastructure components:\u003C/p\u003E\u003Cul style=\"margin-top:0.75rem\"\u003E\u003Cli\u003EDistributed tracing requires trace collectors at multiple system points\u003C/li\u003E\u003Cli\u003EHigh-cardinality data storage needs specialized time-series databases\u003C/li\u003E\u003Cli\u003EReal-time analysis demands additional compute resources\u003C/li\u003E\u003Cli\u003ENetwork bandwidth requirements need increased capacity depending on telemetry volume and system scale\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003ECompared to a monitoring-only approach, expect meaningful increases in bandwidth consumption, compute requirements and data storage costs due to higher telemetry volume and processing demands. Plan for these requirements before deployment.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-16b3d0f0ea","type":"heading3","lines":["Cost considerations"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-da60f6633c","text":"\u003Cp\u003EThe total cost of ownership (TCO) differs significantly between monitoring and observability, driven primarily by differences in data volume, tooling complexity and operational requirements:\u003C/p\u003E\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003ECost Factor\u003C/th\u003E\u003Cth\u003EMonitoring\u003C/th\u003E\u003Cth\u003EObservability\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EInitial setup\u003C/td\u003E\u003Ctd\u003ELower complexity, faster to deploy\u003C/td\u003E\u003Ctd\u003EHigher complexity due to instrumentation and integration\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EData volume\u003C/td\u003E\u003Ctd\u003ELower (aggregated metrics)\u003C/td\u003E\u003Ctd\u003EHigher (high-cardinality telemetry, logs and traces)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EInfrastructure\u003C/td\u003E\u003Ctd\u003ELower storage and compute requirements\u003C/td\u003E\u003Ctd\u003EIncreased storage, compute and network demands\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003ETooling\u003C/td\u003E\u003Ctd\u003EOften fewer, specialized tools\u003C/td\u003E\u003Ctd\u003EBroader platforms required for correlation, storage and analysis\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003ETraining & skills\u003C/td\u003E\u003Ctd\u003EFocused on metrics and alerting\u003C/td\u003E\u003Ctd\u003ERequires expertise in distributed systems and data analysis\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EOngoing operations\u003C/td\u003E\u003Ctd\u003ELower overhead in stable systems\u003C/td\u003E\u003Ctd\u003EHigher due to data management and query complexity\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\u003Cp\u003EDespite these increases, organizations should consider not just direct costs but also the value of faster incident resolution — observability practices are often associated with \u003Ca href=\"https://eajournals.org/ejcsit/wp-content/uploads/sites/21/2025/06/The-Evolution-of-Observability.pdf\"\u003Emeaningful reductions in MTTR\u003C/a\u003E compared to traditional monitoring.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0","h3_title_0","text_1","h3_title_1","text_2","h3_title_2","text_3"]},"section_5":{"columnClassNames":{"text_1":"aem-GridColumn aem-GridColumn--default--12","text_0":"aem-GridColumn aem-GridColumn--default--12","text_3":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","h3_title_0":"aem-GridColumn aem-GridColumn--default--12","text_2":"aem-GridColumn aem-GridColumn--default--12","h3_title_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"migration-strategies",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-c602572303","type":"heading2","lines":["Migration strategies"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-78ff9c2635","text":"\u003Cp\u003EMost organizations build their observability initiatives gradually. Start by auditing your existing monitoring tools and processes. Document your current monitoring coverage across infrastructure (CPU, memory, disk), applications (response times, error rates) and business metrics. Map service dependencies and critical user journeys. Establish baseline metrics before initiating any changes so you have a reference point to measure your progress against.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-83143f7189","type":"heading3","lines":["Planning the transition"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-b096f8309c","text":"\u003Cp\u003EDesign a phased approach that prioritizes high-impact, high-complexity services first — the ones where existing monitoring falls short and incidents are most costly. Define measurable objectives, such as reducing MTTR by 40% or achieving 99.9% trace sampling coverage. Having concrete targets helps you evaluate your progress objectively and sustain support from leadership as the program expands.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-8fdb601560","type":"heading3","lines":["Hybrid approach implementation"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-1f48e38959","text":"\u003Cp\u003ERoll out the transition using a hybrid model in which monitoring and observability systems run in parallel. This approach typically follows this structure:\u003C/p\u003E\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EPhase\u003C/th\u003E\u003Cth\u003EDuration\u003C/th\u003E\u003Cth\u003EFocus Areas\u003C/th\u003E\u003Cth\u003ESuccess Criteria\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E1. Discovery\u003C/td\u003E\u003Ctd\u003E2–4 weeks\u003C/td\u003E\u003Ctd\u003EService mapping, instrumentation assessment\u003C/td\u003E\u003Ctd\u003EComplete dependency map\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E2. Pilot\u003C/td\u003E\u003Ctd\u003E4–6 weeks\u003C/td\u003E\u003Ctd\u003ESingle service implementation\u003C/td\u003E\u003Ctd\u003E95% trace coverage\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E3. Scale\u003C/td\u003E\u003Ctd\u003E3–6 months\u003C/td\u003E\u003Ctd\u003EProgressive service migration\u003C/td\u003E\u003Ctd\u003E\u003C1% data loss\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E4. Optimization\u003C/td\u003E\u003Ctd\u003EOngoing\u003C/td\u003E\u003Ctd\u003EFine-tuning and expansion\u003C/td\u003E\u003Ctd\u003E50% faster troubleshooting\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\u003Cp\u003EFollowing this phased model helps provide checkpoints for validating that new capabilities are delivering the expected benefits before you expand the scope. Be sure to confirm that MTTR and alert noise metrics are actually improving at each stage before moving on.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-ee82534737","type":"heading3","lines":["Success metrics"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-5858eeb39d","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ETrack these key metrics to evaluate if your observability investment is delivering results:\u003C/p\u003E\u003Cul style=\"margin-top:0.75rem\"\u003E\u003Cli\u003E\u003Cstrong\u003EMTTR:\u003C/strong\u003E Compare this metric pre- and post-implemention — this is the clearest indicator of observability’s effectiveness. 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