What is Data Observability and What Does It Mean for DevOps?

What Data Observability Means to DevOps Teams

DevOps teams are tasked with the coordinated role of managing both development and operational aspects of software development systems. They must be able to track how an application performs in real time in order to know how to handle any issues that arise. From the DevOps perspective, observability empowers them to observe changes as they occur in a system. With observability tools and platforms, DevOps can go beyond just monitoring infrastructure and applications to observing real-time behavior of a system and its data. 

This is where the difference between monitoring and observability comes into play for a DevOps professional. Observability is the use of external data outputs to comprehend the current internal state of an IT system, whereas monitoring is simply the process of collecting data from an IT system. Both include collecting diverse data sets, and both help DevOps teams identify problems in their software stacks and help them delivery better UXs. But there are two main differences: 

• Observability Interprets Data, Not Just Collects It

Monitoring is simply the collection of data, whereas observability focuses on interpreting data so those insights can be utilized. Observability correlates data from many sources and identifies patterns or anomalies in the data that are relevant for analysis. 

• Observability Tells You Why Something Is Wrong, Not Just That Something Is Wrong

Monitoring alerts a DevOps team when something in the system breaks down, but data observability goes a step further by helping determine why it failed, and how to best fix it. A monitoring tool, for example, could reveal that an app is responding to requests slower than normal, but an observability tool can show you which specific microservices in an app are actually causing the problem. That information can be used to build a response plan via an incident management solution to improve reliability.  

Types of Data Observability Tools

There are several key categories to data observability tools, including:

• Data Pipeline Tools: for data engineers to build and operate analytical data pipelines. 
• Application Data Tools: for collecting data from apps to improve site reliability, surface performance issues, and build debugging and troubleshooting plans. 
• Machine Learning Observability Tools: for data scientists to improve performance and drift of machine learning models in production. 
• Logs and Event Tools: for using purpose-built pipelines to connect the sources of observable data with the intended destination, as well as improving data in movement. 

DevOps Can Raise Their Game With Data Observability

DevOps teams have always embraced data monitoring, but many have only been able to achieve simple continuous monitoring using pre-developed metrics. There is hope that new observability platforms will help raise their game, helping to identify anomalies that indicate an impending IT issue before it can grow and become a problem. 

Armed with that information, DevOps teams can then determine the root causes and severity of problems and take appropriate action, which is a far cry from the old method of just relying on the process of elimination to determine core issues. With data observability tools, DevOps teams gain greater visibility into their IT and app environment and can unearth better and more actionable intelligence, particularly for external-facing apps that drive business transformation. 

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An Observable Path to Success

So where do today’s tech professionals who want to pursue exciting new fields like data observatory go to learn? There are multiple paths tech teams and individuals can take. One is taking a DevOps Engineer Master’s Program, where learners become experts in the principles of continuous development and deployment, automation of configuration management, inter-team collaboration, and IT service agility using DevOps tools such as Git, Docker, and Jenkins. 

Another option goes down the analytics path with certification in data analytics to learn analytics tools and techniques, how to work with SQL databases, R and Python, how to create data visualizations, and apply statistics and predictive analytics in a business environment. Either path is a worthy endeavor for eager learners, and Simplilearn has the resources aspiring professionals need.