Systems | Development | Analytics | API | Testing

Earth Day gives us all the opportunity to raise awareness, reflect and take action to support a sustainable future. The day, which offers so many ways to get involved, means many things to different people. Fortunately, in the data infrastructure business, there are many areas where we can make improvements to support resilience, efficiency and optimization. At Hitachi Vantara, we’re committed to the principles of sustainability and responsible business practices.

There is a well-worn adage attributed to famous early American statesman and inventor Benjamin Franklin, who purportedly said, only two things in life are guaranteed: death and taxes. So it’s not without some sense of irony – Tuesday having been “Tax Day” in the U.S. (April 15, the day annual income taxes are due) – that we can proudly proclaim there are now officially THREE things in life that are guaranteed! What’s the third one, you (and Mr. Franklin) might ask?

Achieving cyber resilience is a constant challenge for even the largest and most experienced organizations and IT leadership teams. Studies, including one from our partner Veeam, show +75% of organizations experienced a ransomware attack in the past year. Those that didn’t are well-aware they may be next.

Every article I read highlights the need for rapid adoption of AI, with emphasis on agentic and generative AI redefining the boundaries of data architectures. As organizations expand automation, personalization and real-time decision-making, they face a fundamental challenge: data gravity – especially to meet the demands of AI reasoning inference workloads – is pulling AI workloads toward storage, but disaggregated architectures struggle to keep pace with the need for rapid data access.

When you’ve worked with data at scale, you come to appreciate the beauty and the complexity of systems like Kafka. With nearly 30 years of experience navigating the evolution of technology and data platforms, I’ve seen firsthand how Kafka has revolutionized real-time data processing - and where it can challenge even the best teams. The real question is: How do you move past those challenges to unleash everything Kafka has to offer?