What is Observability and why you may need it?
Published: 2021-11-22 06:59:59

What is Observability

What is meant by Observability and how has it evolved into something which has transformed the entire IT ecosystem. We will break down all the information in this blog.

Before Agile methodology was born, <a href="https://www.twine.net/find/web-developers">developers</a> also used something called <a href="https://en.wikipedia.org/wiki/Test-driven_development">Test-Driven Development</a> during the traditional software development life cycle. It is a software development process with a repetition of short development cycles until the test cases pass. Over the years, the TDD and Production together became Observability Driven Development(ODD)

For starters, Observability does not mean monitoring, it's more than that. Observability helps you understand what is happening inside your IT infrastructure and environment without actually writing a new piece of code. It also helps the users know which state the system is in now and how it is functioning. Observability is an important concept in modern IT operations, where the goal is to monitor what's happening within a system and provide timely feedback.

The core of the Observability, also commonly known as the three pillars of Observability is:<ul>
<li>Metrics</li>
<li>Logs</li>
<li>Traces</li></ul>

Example of Observability
Suppose you are monitoring web servers. You would want to know if any of them were experiencing CPU or memory problems, or if they’ve gone offline for any reason. If the server crashes, it should automatically restart without human intervention. The idea is that systems should be self-healing and self-managing as much as possible. This will not only save money by reducing human labor costs but also improve reliability dramatically because there won't be any single point of failure.

Data Observability
The data observability helps in maintaining the health of the IT infrastructure systems by monitoring, tracking, and troubleshooting the incidents and at the same time prevent other problems from occurring.

So the ideal data observability tool which organizations plan on using should ideally handle all these kinds of data. 
This Observability data generally has:<ul>
<li>High cardinality</li>
<li>Wide structured events(Not metrics and logs)</li>
<li>No indexes, schemas, or a predefined structure</li></ul>

Why building your own tool is not a good idea:

Traditionally in monolithic architecture-based systems, it was easy to handle the data, but the complexity is soaring. The important challenge in building on open source is always to be conscious of the data types operations groups need to gather, refine and handle data flooding from firewalls, containers, SNMP traps, and HTTP sources. In addition, you will need to fetch info from Kafka and other messaging resources. If we use open source we are constructing every part of the data processing pipeline. The known-unknowns and the unknown-unknowns are increasing rapidly.

Some of the main challenges are:<ul>
<li>Distributed and replicated data</li>
<li>Increased adoption of microservices</li>
<li><a href="https://middleware.io/blog/what-is-autoscaling/">Autoscaling</a></li>
<li>Multiple failovers</li>
</ul>
Hence, implementing an observability platform might be a best practice for high-performing teams with complex architecture. It enables everyone in the team to inspect and identify the root cause of a problem by even looking at the function and request level. This gives more significant insights for the ITOps and DevOps teams and helps prevent such instances in the future.

Using an AIOps platform will help you gain more insights from these metrics and find the root cause of a problem. You can even forecast using the built-in ML algorithms we have and predict the possible spike in usages which will help enterprises save a fortune in the server costs.
 
The edge which AIOps provides are<ul>
<li>Native observability support plus 3rd party tools integration</li>
<li>AI/ML capabilities to the edge and closer to the data for faster response and efficient data handling</li>
<li>ML-driven symptom and stack-based correlations provide very targeted & faster noise reduction</li>
<li>Built-in full-stack discovery & mappings for automated alert enrichment.</li></ul>