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Observability and reliability of the systems is becoming more and more dependent on automation. Through the elimination of manual work, the enhancement of response time and the prevention of emerging issues, automation has transformed the ways organizations deal with their IT environments. The use of automation in key systemshas increased efficiency and reduced the chances of errors and system failures.
Rohith Samudrala, a top professional in his field, has utilised scripting tools to increase efficiency in processes while decreasing manual efforts. He emphasised that automation is key to modern observability, enabling proactive insights, anomaly detection, and root cause analysis while maintaining human oversight.
Samudrala mentioned that he “designed and implemented automated health checks for critical systems, reducing manual intervention by more than 40%”.The automation of health checks, anomaly detection, and dashboard creation through the creation of complex scripts has made IT operations much easier by cutting down on time and effort. He simplified log analysis and system diagnostics, freeing up 12-15 hours a week for each team. The same automation in anomaly detection has also reduced Mean Time to Resolution (MTTR) by up to 50% to ensure that any possible problems are detected and addressed before causing inconvenience to the end-users. The outcome has been the marked increase in the availability with organizations now experiencing 99.8% uptime, which is approximately 20 minutes of downtime per month.
The essence of this transition led by him is the synthesis of scripting tools with sophisticated observability platforms such as Dynatrace, ThousandEyes, and Evolven. This integration also makes it possible to monitor the different systems in unison so that any problem can be noted in the real-time. He integrated Infrastructure as Code (IaC) to deploy observability tools, reducing setup time from weeks to days, eliminating human errors.
AI is used in monitoring and anomaly detection which has greatly improved operational reliability particularly during high demand or adverse weather conditions. Automation of scaling resources along with auto-healing mechanisms have ensured that the important applications remain up and running and load levels do not go very high, thereby bringing down the downtime almost to negligible levels. The transition to proactive, automated IT management has taken people away from the reactive mode, providing a culture that focuses on potential problems before they become problematic, which has helped to build more confidence in IT.
However, there have been some challenges along the way to the development of full automation, although the benefits of such a solution are obvious. The main challenges were the implementation of automation solutions in a team and the integration with existing systems. These problems were solved during the workshops, training, and creating modular and scalable scripts for the work of teams with the help of the automated systems. The use of artificial intelligence and machine learning went further to solve the problem of much data by first sorting out noise and more importantly, alerting teams to what they need to know.
As is suggested by industry leaders like Rohith Samudrala, the observability will be even more automated. AI is expected to take an even more significant proportion in terms of preventing and solving problems before they occur to the users. The current systems are gradually becoming distributed and intricate and, thus, require automation to sustain the system’s integrity.
In conclusion, the future of observability platforms is to be even more interactive and the user-friendly software that non-technical persons will be able to use for making pertinent decisions. Automation in observability is not just about ‘doing more with less’, it is about proactively enabling new value, delivering consistent digital experiences, and preparing organizations for the future digital environment.
