The need for scalability, agile development, continuous integration(CI), and continuous delivery(CD) emphasize the value of observability tools. Observability tools are machine learning integrated technologies that help administrators visually understand the behaviors of an application and measure its performance with the provided telemetry data: logs, metrics, and traces.

Observability tools help to diagnose breaking performances even in complex and highly distributed infrastructure to prevent downtime. This means avoiding unnecessary bugs and problems, providing a better end-user experience, and aiding better organizational decisions and achieving goals.

Every observability tool has necessary and extra features. However, the focal point should be the end result — this article creds to the ideal evaluation benchmark for choosing the right vendors. You would not want to go out with high expectations and get a bad result with a massive toll on your financial and technical capacity.

However, before delving into the nitty-gritty of this note, the next section will briefly describe the five pillars that data observability is predicated upon for all types of telemetry data you collect. These pillars will help you ask quality questions and address your needs without much fluff:

Freshness

Freshness means updated or new data, the timestamp of data generation. Freshness begs the question of gaps in the updates. If not correctly addressed, it can result in the breakage of a data pipeline.

Distribution

Is the data well laid out or formatted? Is it detailed and complete? Distribution relates to your data’s health. It uses null values to measure the data distribution chain. When the new values proliferate out of the allotted proportion, there’s a problem in the data’s distribution or an abnormal representation.

Volume

This is the available or total amount of files in a database. You use this to measure your data health when it is not meeting a required expectation. The completeness of a database or table offers insight into your data health. When 5 million rows suddenly turn into 1 million, you should understand there’s a problem.

Schema

This fourth pillar, schema, is a visual structure or formal text-based description of a DBMS. It describes the data and how it relates to other models. Optimizing your observability tools for the health of your data means you’ll have a robust audit of your schema.

Lineage

This last pillar puts all the other pillars in a way for you to map out your data ecosystem. When data breakage occurs, the data lineage says “were,” telling which upstream or downstream sources were affected, the teams generating, and persons accessing the data. In a nutshell, it tells a story about the health of your data.

Here are the four questions you ask in evaluating observability tools:

What kind of data does the observability tool store?

After choosing what you intend to monitor, the goal is to bring full visibility to your data systems to optimize cost and performance. And the first step to evaluating an observability tool is knowing the kind of data it stores and supports and if that aligns with your business requirements, including your past concerns and future needs. Not all observability tools store data similarly — especially open-source technology. Some have defined metrics and tracing, some are good at visualization, and others are good at efficiently storing events and metrics data.

However, the intersecting focus is on the end user’s experience and satisfaction with the application, primarily when your user directly interacts. How much work will it be to integrate with my things? After instrumenting your code, this question dives deep into the importance of interoperability between the software application, environment, or infrastructure and the observability tool for troubleshooting and enhanced performance.

Sometimes, building secure applications and harnessing the data value may demand new pipelines on a flexible tool like Prometheus that provides real-time event streaming, a scalable platform like Loki, or even a free tool like Elastic that affords cloud-native integrations and replication of data in hybrid environments. Choosing a data integration tool that facilitates test-driven development, checks for dependencies and inaccuracies, and alerts you on anomalies is expedient.

How easy is it to access/query the data?

Organizations understand the benefits of accessible data and the repercussions without it — with the nuanced structure of data pipelines, your data’s health should be understandable and discoverable. With this approach, engineers and stakeholders can collaborate on exploring relevant information on deep analysis and root causes.

Your team needs access to necessary data incidents and resolving actions that manage the problem. Having an infrastructure that connects to the communication channel of the required personnel in real-time, even streaming and alerts, can also be beneficial in troubleshooting and preventing data downtime.

How can I integrate/crossmatch with other sources?

A good observability tool should have collaborative or extensible functionalities because the tool feeds on a lot of data and can have several sources for analysis like relational databases, Saas tools, metadata, etc. From several sources, observability quickly analyzes and identifies current and potential problems. This brings one of the five pillars of observability to light, Lineage. Data lineage assesses upstream, downstream sources and investors to improve data management and follow data governance principles. The pipeline sources observe relevant data to analyze usage patterns, cost, operational aspects, and reliability.

What are the Pricing, Total Cost of Ownership, and Training of Team Members?

Consider the parameters you can measure from this moment to evaluate your costs. Costs grow as the infrastructure scales upward. Some charge per event, some per host, some per active service, and many vendors charge in data size — Amazon charges up to $0.09 / GB for egress data on AWS.

Examine the value or the ROI that the generated data provide so the unnecessary metrics can be reduced to optimize the cost of the collected data. This will make teams and stakeholders more confident in the infrastructure where it is sensible that the required data brings in the required value rather than pay for what you don’t use. For example, picking up a tool like Prometheus because it’s free and open source means you have to calculate your costs and measure your capability with the knowledge or required training of your team members for a given period.

Sometimes, new tools that fit your requirements may need to be adopted because good organizations use good tools and train scarce and valuable engineers to solve their core business concerns. Understand your infrastructure’s components, budget, costs, and skill sets of the team and the invisible human costs. Choosing the best among a plethora of observability tools is not an easy task. But you can examine your concerns, requirements, and objectives with the answers to these five questions that offer a holistic approach.

Rather than guessing and taking chances randomly, different observability tools should be used in line with your environment and software application or objectives. Observability tools provide the support that makes it possible to diagnose and address unexpected problems before they morph into serious issues while establishing a good relationship with your customers/clients. Nonetheless, evaluating your observability tools to optimize cost and performance is vital in this modern age of data-driven innovations.