Ultimate Guide to Observability for Businesses

It’s hard to fix something you can’t see — which is where observability comes in. Observability uses logs, metrics, and traces to help organizations gather useful information in their operations processes that can unveil issues.

Steve Bigelow: It’s hard to fix something you can’t see if your car breaks down a mechanic can pop the hood and look inside. But what if an application misbehaves or an organization’s cloud network goes down, how do you track and troubleshoot problems. This is where the concept of observability comes into play. By helping the responsible team gain insight into what’s going on with the system, so they can identify the cause of a problem, and then fix it. The challenge of observability is not so much determining the internal state of a system from observations, but rather collecting the right observations.

Here, we’ll go over what organizations need to know about observability and how it impacts business. But for a deeper dive, click the link above or in the description below to explore our complete collection on all things observability. It sounds straightforward enough, but observability is actually rather complex. For one thing, observability is reactive in that it prioritizes existing critical data. But observability is also proactive where necessary, meaning it includes techniques to add visibility to areas where it might be lacking. Visibility is the ability to peel back the layers of a system or infrastructure to gain useful information. monitoring tools and practices play a vital role in this, providing the scope and depth needed to gather health security hardware, host operating system, and other data needed to gain visibility. Visibility requires both policies and tools. Visibility policies guide what data is gathered policy also defines how data is secured and retained. Good policies can help prevent an organization from gathering everything and keeping it forever, which as you might imagine, can turn data and analytics into an unmanageable swamp. Remember, more isn’t always better.

Visibility is about seeing what’s important, not seeing everything. The tools for visibility are the software applications used to gather and aggregate data from systems, networks, applications and platforms. In any discussion of observability, you’ll likely hear a reference to three external measures, sometimes called the three pillars of observability. These pillars are the three primary source data types, logs, metrics, and traces. logs or the first pillar refers to records of events typically in text form. Some applications will log what the developer believes represents critical information. log information tends to be historic or retrospective, often used to establish context in operations management. Metrics. The second pillar refers to real time operating data that’s usually accessed through an API using a poll or polling strategy, or might be accessed as a generated event or telemetry, a push or notification for example, because metrics are event driven, most fault management tasks are driven from metrics and traces.

The third pillar of observability are records of information, pathways or workflows. These pathways or workflows are designed to follow a unit of work like a transaction through the sequence of processes that application logic directs it to follow. Some trace data might be available from workflow processes, such as service buses, or cloud native micro services and service meshing, but it might be necessary to incorporate trace tools into the software development process to gain full visibility. All three pillars, logs, metrics, and traces are vital to observability. But each has certain limitations. Logs, for example, can be challenging to sort and aggregate in order to draw meaningful conclusions or relationships. metrics can be hard to tag and sort. And traces often produce enormous amounts of data. Some of that data may be necessary to gain observability, but a lot of it might not be. So how do you overcome these limitations and difficulties, it helps to focus on goals. By this I mean, it helps to set the business objectives and then set observability goals that align with those objectives.

For example, if the business is concerned with latency or throughput, then set appropriate latency or throughput goals, and then use the three pillars, logs, metrics and traces to help reach those goals. The primary benefit observability is improvements to the user experience. Specifically, proper use of observability can improve application availability and performance. observability practices may also reduce operations costs with observability problems can be solved faster, and that means spending less staff time and other resources on them. But observability comes with some challenges too. One challenge is something called accidental invisibility. That’s when data sources aren’t properly filtered or structured. This can cause important information or a critical condition to be missed because it’s hidden from view. Another headache is when there’s a lack of important source data. This happens especially at the application level. And with the tracing of workflows, it can also be difficult to assemble the right information, and interpret what’s available when the same type of data comes in different formats from different sources.

That’s why an organized strategy for structuring information into a standard form is essential to observability. You can minimize challenges and reap the benefits of observability. by beginning with a plan. The first step is to identify the specific benefits desired, then link each benefit to the type of data that would be needed to achieve that benefit. It’s important that these links between benefit desired and data needed. Consider the data you have available through monitoring and telemetry. But it’s equally important to identify what information is not currently gathered or is being gathered in the system that isn’t contributing its data for observability analysis. After you have an observability plan in place, the next step is to create an observability architecture. This is a diagram of the relationship between the source data and how the data is presented to operations personnel, AI systems, machine learning systems, and so on. It’s essential to identify all data sources along with the information that each source is expected to contribute.

The architecture diagram should also identify the tools that collect and present the information, the tools for data analysis and filtering, and the tools for data presentation. The final step and implementation is to choose your observability toolkit or platform. And observability toolkit is a set of monitoring tools are features that support observability but need a human operator or a separate software layer to support collective analysis. toolkits usually require customization, but they can also accommodate your existing software and data sources. And observability platform is an integrated software application that collects information, performs analysis, and presents actionable results to operations users, a platform might still require customization to accommodate all the data sources available. It might also constrain the way data is integrated, ultimately, which observability tool or platform you choose depends on your organization’s needs, data sources, and budget. Achieving observability isn’t easy. It requires ingesting and sorting through an enormous amount of data, and then performing analytics so you get clear actionable information.

The sheer volume of raw data, especially from multiple sources, makes analytics difficult and resulting output as little value if it doesn’t actually tell the business anything it wants to know. To up the odds that your observability initiative will be effective. I’d like to leave you with five suggestions. First, set goals for observability. Understand what’s being observed and why and what the intended benefits are for the business. Second, curate the data. By this I mean, be sure the data that’s generated and ingested is relevant to the goals set for your observability initiative, review the data sources, consider adding context or altering data collection to benefit observability. This might require aggregating or rolling up some data so you can more easily see trends in a time series. Third, seek meaningful and actionable outputs. Details are easily lost in the noise of daily business. So look for meaningful data to produce actionable outputs. Fourth, configure outputs appropriately configured reporting, alerting and dashboards so they prove I’d meaningful and actionable outputs. For example, rather than setting static alerting thresholds, configure time parameters that might forego an alert if the parameter returns to normal within a given time, which can cut down on noise. And finally, be sure to consider who’s receiving the observability conclusions. For example, reports might go to one admin non critical alerts might go to another admin and critical alerts might be directed to a third. Take the time to be sure the right people see the right outputs and nothing important is inadvertently ignored.

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