Artificial Intelligence and Machine Learning have been at the heart of our strategy since the beginning. At the birth of Nexthink we wanted to help IT teams not get drowned in list of logs, but rather immediately see actionable data already processed, correlated and ready to consume.
Thanks to the economic downturn and rising interest rates, leading financial industry stalwarts such as Accenture, BlackRock, BNY Mellon, Goldman Sachs, and PayPal have already laid off hundreds or thousands of employees. And these are just the big names; banks, credit unions, insurers, and private equity firms of all sizes worldwide are carefully reviewing their headcount and trimming their workforce in order to stay competitive.
High Cardinality woes are far & frequent in today's modern cloud-native environment. What does it mean, & why is it such a pressing problem?
The advent of multi-cloud and hybrid-cloud architectures has created new opportunities for organizations to leverage best-in-class features from various cloud service providers. However, these complex environments present their own unique challenges, especially when it comes to monitoring and managing performance.
In the eternal hunt for elusive bugs, logging is an indispensable aid. By recording the events and messages that occur during the execution of your program, logging opens the door to unparalleled debugging and performance monitoring capabilities. It all starts with Python’s built-in logging module. However, the true power of Python logging is unlocked not merely by using it, but by mastering it.
The financial impact of Internet outages on businesses is well recognized. Yet, the exact cost remains difficult to gauge due to the individual nature of each company, its environment, the industry, risk tolerance, and so on. A significant breakthrough in understanding this cost has been achieved through a recent commissioned study conducted by Forrester Consulting on behalf of Catchpoint, entitled, Increase Revenue with Internet Performance Monitoring.
