{"id":50726,"date":"2022-04-25T00:00:00","date_gmt":"2022-04-25T00:00:00","guid":{"rendered":"https:\/\/www.techopedia.com\/how-to-choose-the-right-application-monitoring-tool\/"},"modified":"2022-07-25T19:39:19","modified_gmt":"2022-07-25T19:39:19","slug":"how-to-choose-the-right-application-monitoring-tool","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/how-to-choose-the-right-application-monitoring-tool\/2\/34736","title":{"rendered":"How to Choose the Right Application Monitoring Tool"},"content":{"rendered":"

Application performance monitoring<\/a> (APM) is a key focus for IT teams today\u2014and for good reason.<\/p>\n

Tracking metrics such as response time<\/a>, transactions<\/a> per second and requests per second are an important user experience<\/a> indicator. But APM has a major limitation: It tells you if<\/em> your system is not working, not why<\/em> it isn’t. Without analyzing your logs<\/a> and metrics for deeper insights, your business won’t have the necessary information to optimize your level of service<\/a>.<\/p>\n

The solution? Observability<\/a>.<\/p>\n

Observability tools can be divided into two distinct categories:<\/p>\n

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  1. Single-pane-of-glass<\/strong><\/a> solutions<\/strong>, which prioritize simplicity and convenience.<\/li>\n
  2. Observability pipeline solutions<\/strong>, which maximize the amount of data you can aggregate and analyze.<\/li>\n<\/ol>\n

    Which method of application performance monitoring is better for your business? Unfortunately, there’s no one answer. Indeed, both the above-mentioned categories have essential roles to play in modern observability strategies\u2014and you may need to deploy them simultaneously to meet your needs.<\/p>\n

    Let’s break down the functionalities of single-pane-of-glass tools and observability pipeline tools, respectively, and help you decide which one is best for you:<\/p>\n

    1. Single-Pane-of-Glass Tools<\/span><\/h2>\n

    A single-pane-of-glass<\/a> observability tool provides a central console<\/a> where you can track data analytics<\/a> and alerts. The data<\/a> itself typically comes from multiple sources\u2014such as logs<\/a> and metrics from various parts of your infrastructure<\/a> and applications<\/a>\u2014 but the observability tool consolidates it around a single interface<\/a> and set of dashboards<\/a>.<\/p>\n

    Most single-pane-of-glass solutions aim to provide some ability to \u201cdrill down\u201d into particular data sets<\/a> to gain more context on a pattern<\/a> or anomaly<\/a> or explore how different data sets relate to each other. (Also read: <\/strong>How To Train Your Anomaly Detection System To Learn Normal Behavior in Time Series Data<\/strong><\/a>.)<\/strong><\/p>\n

    However, because all data needs to fit within a central interface, the level of analytical nuance and depth single-pane-of-glass tools support is nuanced.<\/p>\n

    That is also the case with the volume of data these tools can collect and the variety of data formats and types they support. A single-pane-of-glass solution typically can\u2019t capture and analyze every data stream<\/a> from every resource in your IT<\/a> estate. Instead, it focuses on the most important and accessible ones: production applications and infrastructure as well as systems that expose metrics and logs in an easy-to-collect way.<\/p>\n

    The tradeoff for limited analytics depth and data coverage is that single-pane-of-glass tools are easier to use<\/a>: There\u2019s only one interface to learn and a small volume of data collectors<\/a> to configure.<\/p>\n

    2. Observability Pipeline Tools<\/span><\/h2>\n

    An observability pipeline tool collects and analyzes observability data in your IT estate. From there, the goal is to enrich it and direct it wherever makes the most sense for analysis.<\/p>\n

    Instead of requiring all data to be analyzed in a single console, using whichever methods that console supports, observability pipeline tools provide the flexibility to analyze data in many different ways. Different teams within an organization can interpret the same data sets in different ways\u2014which may be helpful if, for instance, your IT team wants to observe resources for performance management<\/a> purposes while your security team<\/a> wants to monitor the same resources to detect security risks<\/a>. (Also read: <\/strong>The 7 Basic Principles of IT Security<\/strong><\/a>.)<\/strong><\/p>\n

    Just as important, observability pipeline tools let you work with data in various formats. Whether you\u2019re dealing with conventional data from applications or infrastructure, or data sources like data streams from IoT<\/a> devices<\/a>, observability pipelines support them all.<\/p>\n

    Observability pipeline tools may provide some built-in analytics features. But they are flexible enough to work with whichever third-party analytics tools<\/a> your teams prefer to use.<\/p>\n

    Although observability pipeline tools are more flexible and scalable<\/a>, they\u2019re no more difficult to configure than single-pane-of-glass tools. After establishing the data connectors, you can work with any data in any volume.<\/p>\n

    You will need to set up multiple data destinations. Still, the tradeoff for that effort, as noted above, is that you gain the flexibility of analyzing your data in whichever ways you need\u2014instead of being limited to one console and one set of features. (Also read: <\/strong>Destroying Silos With Integrated Data Analytics Platforms<\/strong><\/a>.)<\/strong><\/p>\n

    How to Choose<\/span><\/h2>\n

    Both types of observability have their place within modern observability strategies. Depending on your organization\u2019s observability requirements, a particular tool may work better than another\u2014or you may need both.<\/p>\n

    Single-pane-of-glass observability solutions may be best for your business if:<\/p>\n