Ok! The heading of the topic could be misleading or confusing. But the message that I wanted to convey is the single core principle that anyone should follow when building an observability solution for their company. Set up data observability from beginning to end by building a solution for multiple types of systems that can be interpreted as a single system. The solution should offer unified, comprehensive observability across all of your data storage locations, regardless if it’s in databases, data warehouses, data lakes, data streams, or files.
Any company that runs its own information systems or works with a lot of complicated data that requires building complex data pipelines, must be able to quickly look at, understand, and fix data outages. This cannot be accomplished within the confines of a single technical system. To manage the collection of data from multiple sources and its transformation into a single, authentic version, you need a wide range of programming methods, back-end systems, communications protocols, operating systems, and server types. In this situation, I don’t think it would be smart to only let data be seen from a single source. However, to comprehend observability data from distributed systems quickly, you would need to bring it to a single platform, and correlate it to form a chain of time-based sequences in a single view. Perhaps the mind map in the following link will illustrate what I am talking about.
Let’s say you have an idea of the core principle stated above, and now if you want to come up with a good solution for data observability, you should start by building the right components for it. For that, you have to keep the following points in mind.
1. Sources of Information: Metrics, logs, and traces are always mentioned as the cornerstones of data observability. However, because distributed systems are so complex, they can have many interrelated failures that are difficult to detect. As a result, it is critical to maintaining accurate records in order to ensure service continuity. As a result, you must understand which sections of the system(s) or areas of the system(s) may include information that you can leverage. Those may not yet be available to you, but they must be.
2. Make it as real-time as possible: You need to design a system that is able to receive information rapidly, evaluate those and discover potential issues and trends as soon as they happen. I believe you should develop solutions that are driven by events as they occur. Yes, there would be obstacles, but with the assistance of a combination of open-source technology, you can come close to achieving it.(even if it is not 100%)
3. No need to build everything from scratch: Our open-source community is very strong and they are building tools for catering to the specific needs of any technological situation. I would suggest you should not try to reinvent the wheel, embrace open-source. But the caveat is you would need to package them to work together so that they solve a bigger problem of distributed data observability requiring unified tracking. This is where most of the efforts should go in.
4. Usability first approach: Well, we are definitely building a complex technical solution, but you can not get away from the fact that sleek technical solutions are no good if they are not usable. Hence, before building a solution go for solving the problem of usability first. The what part needs to be very clear and should be defined keeping in mind stakeholders that are going to use it. This will also include designing simple, efficient, and workflow-oriented user experiences.
At Atgeir Solutions, we are building a data observability product based on the core principle mentioned above and the most important parts of building a data observability solution. “HawkEye” is a complete data observability solution that we made after getting a lot of customer feedback and working with them. After making a product for data observability, I felt compelled to share the most important things I learned with the people who read this article.
