“40% of all enterprise workloads will be deployed in CIPS [cloud infrastructure and platform services] by 2023, up from only 20% in 2020.”.As the cloud permeates every aspect of business, decision-makers must make critical choices regarding infrastructure at every turn. Their answers will ultimately determine if every part of an organization is empowered to move forward in a cohesive way to reach business outcomes.
For our latest specialist interview in our series speaking to technology leaders from around the world, we’ve welcomed James Kaplan CEO and Co-Founder of MeetKai. He founded the startup with his Co-Founder and Chairwoman, Weili Dai, after becoming frustrated with the limitations of current automated assistants. Kaplan has had a true passion for AI and coding since he was six. He wrote his first bot at only nine years old and wrote the first original Pokemon Go bot.
Suppose that you work for the infosec department of a government agency in charge of tax collection. You recently noticed that some tax fraud incident records went missing from a certain Apache Kafka topic. You panic. It is a common requirement for business applications to maintain some form of audit log, i.e. a persistent trail of all the changes to the application’s data. But for Kafka in particular, this can prove challenging.
Suppose that you work for a government tax agency. You recently noticed that some tax fraud incident records have been leaked on the darknet. This information is held in a Kafka Topic. The incident response team wants to know who has accessed this data over the last six months. You panic. It is a common requirement for business applications to maintain some form of audit log, i.e. a persistent trail of all the changes to the application’s data to respond to this kind of situation.
Joint customers can now stay within AWS for all cloud services and minimize data movement costs.
Historically, maintenance has been driven by a preventative schedule. Today, preventative maintenance, where actions are performed regardless of actual condition, is giving way to Predictive, or Condition-Based, maintenance, where actions are based on actual, real-time insights into operating conditions. While both are far superior to traditional Corrective maintenance (action only after a piece of equipment fails), Predictive is by far the most effective.
What can we say: Research is non-linear, there are tests, and adjustments, and more tests, and more adjustments, and then we add more data, and test some more, and… you know the story.
