Cloud monitoring and observability can involve all kinds of stakeholders. From DevOps engineers, to site reliability engineers, to Software Engineers, there are many reasons today’s technical roles would want to see exactly what is happening in production, and why specific events are happening. However, does that mean you’d want everyone in the company to access all of the data?
The ultimate success of any security monitoring platform depends largely on two fundamental requirements – its ability to accurately and efficiently surface threats and its level of integration with adjacent systems. In the world of SIEM, this is perhaps more relevant than any other element of contemporary IT security infrastructure.
Good logging practices are crucial for monitoring and troubleshooting your Node.js servers. They help you track errors in the application, discover performance optimization opportunities, and carry out different kinds of analysis on the system (such as in the case of outages or security issues) to make critical product decisions. Even though logging is an essential aspect of building robust web applications, it’s often ignored or glossed over in discussions about development best practices.
As developers, we would like our users to interact with applications that run smoothly and without issues. We want the libraries that we create to be widely adopted and successful. All of that will not happen without the code that handles errors. Java exception handling is often a significant part of the application code. You might use conditionals to handle cases where you expect a certain state and want to avoid erroneous execution – for example, division by zero.
Anyone tracking the evolution of the IT industry is probably familiar with the concept of Industry 4.0. Essentially, it describes the process by which traditional industrial tasks become both digitized and continually managed in an IT-like fashion via modern technologies like cloud computing, digital twins, Internet of Things (IoT) sensorization, and artificial intelligence/machine learning.
MEAN is evolving as a popular web stack for developing cloud native applications because of its scalability, ease of extension, and high reliability. Each component in MEAN is built on JavaScript, contributing to a cohesive development platform. In this post, we take you through the log management options that are available for each component of the MEAN stack framework and their respective limitations – limitations that are addressable with a refined log management solution like observIQ.
First of all, don’t ask this! Instead of asking what to log, we should start by asking “what questions do we want to answer?” Then, we can determine which data needs to be logged in order to best answer these questions. Once a question comes up, we can answer it using only the data and knowledge that we have on hand. In emergent situations such as an unforeseen system failure, we cannot change the system to log new data to answer questions about the current state of the system.
