It may seem redundant to say that DevOps and IT operations teams will encounter new challenges in the following years because their primary role is to address problems and overcome obstacles. However, coping with the current environment of processes, technologies, and tools has become tricky due to the rapid rate of change. Furthermore, business users’ pressure on DevOps and IT operations teams is immense, demanding that everything be solved with a single tap on an app. Handling difficulties in the backend, on the other hand, is a whole new ballgame; people have no idea how tough it is to locate and fix a problem.
One of the most challenging tasks facing today’s DevOps and IT operations teams is identifying minor but potentially dangerous errors in enormous streams of Big Data being logged in their environment. Simply put, it’s like looking for a needle in a haystack.
If you work in the IT department of a firm that has a 24/7 online presence, you may be familiar with the following scenario. Assume you receive a call late at night from an angry client or your employer complaining about a failed credit card transaction or an application crash. You immediately go to your laptop and access the log management system. You see, at the specified time frame, there are over a hundred thousand messages logged – a data set that would be difficult for a human to go through line by line.
So, what do you do in a case like this?
Every DevOps and IT operations teams expert has experienced it: they spend many sleepless hours sifting through a sea of log entries searching for actual events that caused an inevitable occurrence. This is where centralized and real-time log analytics come in handy. It aids them in comprehending the most crucial components of their log data and quickly identifying the significant concerns. The troubleshooting process becomes a walk in the park, making it faster and more successful and allowing professionals to foresee future issues.
IT operations are becoming increasingly complex as they become more agile and dynamic. Because the human mind can no longer keep up with the velocity, volume, and variety of Big Data flowing through daily operations, Artificial Intelligence (AI) has emerged as a vital and robust tool for optimizing the analyzing and decision-making processes. AI bridges the gap between humans and Big Data, providing them with the operational intelligence and speed they need to considerably reduce the strain of troubleshooting and making real-time decisions.
AI And Its Effect On IT Operations And DevOps
Businesses require a solution that enables DevOps and IT operations teams to swiftly identify problems within the mountains of log data entries, as outlined at the outset. Wouldn’t it be simple to find that one log entry that’s causing problems in the environment and crashing your applications if you just knew what kind of error you’re searching for to filter your log data? Of course, the quantity of work would be slashed in half.
One method to accomplish this is to create a system that mimics how a user examines, monitors, and troubleshoots situations, allowing it to learn how humans interact with data rather than interpret the data itself. One alternative is to use a platform that has gathered data from the internet about various relevant situations, watched how people with similar setups solved them in their systems, and examined your system for potential issues. This technology could, for example, be equal to Amazon’s product recommendation system and Google’s PageRank algorithm, but it will be focused on log data.
Cognitive Insights: An Overview
A recent technological advancement has enabled the implementation of the solution envisioned in this article. Cognitive Insights is the name of the technology which has recently gained a lot of attention. This ground-breaking system combines open source repositories, discussion forums, and social threads with machine-learning algorithms to match human domain expertise with log data. It creates a data reservoir of valuable insights from this data, offering solutions to a wide range of crucial issues faced by IT operations and DevOps teams daily.
Obstacles In Real-Time
Many issues plague DevOps engineers, IT Activities managers, CTOs, VP engineering, and CISOs, which can be efficiently minimized by integrating Artificial Intelligence (AI) in log analysis and related operations. While Cognitive Insights can be used in a variety of ways, the following are the two most common ones:
DDoS (Distributed Denial of Service) assaults are becoming more widespread. What was once reserved to governments, high-profile websites, and multinational corporations is increasingly targeted by prominent individuals, small businesses, and mid-sized businesses.
To protect against such assaults, a centralized logging architecture that can detect unusual activity and pinpoint potential risks from hundreds of entries is critical. Anti-DDoS mitigation via Cognitive Insights is highly successful in this regard. Leading companies like Dyn and British Airways, which DDoS attacks have previously harmed, now have a full-fledged ELK-based anti-DDoS mitigation plan in place to keep hackers at bay and protect their operations from future attacks.
Wouldn’t it be fantastic if you could have all of your logs in one location, with each entry being carefully watched and recorded? Without a doubt. You’ll be able to see the process flow in detail and run queries on logs from several apps all in one location, resulting in a significant increase in the efficiency of your IT operations. One of the most challenging problems that DevOps and IT operations teams face is identifying minor but potentially dangerous faults in their environment’s enormous log data streams. This is precisely what Cognitive Insights aims to do. Because this program’s core is built on the ELK stack, it sorts and simplifies data, making it simple to get a clear picture of your IT operations. Asurion and Performance Gateway are two excellent instances of companies that have benefited from Cognitive Insights and raised their IT game.
The Benefits Of AI Integration
It becomes much easier to discover the needle in the haystack and address problems using AI-driven log analytics tools. Such a system will significantly impact the entire organization’s management and operations. As with the company issues outlined before in this blog, combining Artificial Intelligence (AI) with a log management system will help in the following areas:
- Increased client satisfaction
- Customer service and monitoring
- Resource optimization and risk reduction
- Make logging data accessible to increase efficiency.
- To put it another way, Cognitive Insights and other similar technologies can help manage data logs and troubleshooting.
AI has already altered the way IT companies do business. It has now entered the DevOps sphere to fully realize its promise by making the SDLC more intelligent, increasing team velocity, and eliminating human errors. DevOps teams may reap the benefits of autonomous self-learning systems at every level of the DevOps development cycle by using Artificial Intelligence (AI).
To know more about artificial intelligence(AI), contact the ONPASSIVE team.