Systems | Development | Analytics | API | Testing

AI is redefining what it means to build data-driven businesses. It’s no longer about mining insights from data—it’s about creating intelligent systems that can understand the state of the business and act on its behalf in real time. And in this new era, context data is king. That was the recurring theme at Current New Orleans, the data streaming event that drew in thousands of attendees, in-person and online.

As a critical, stateful system, migrating Apache Kafka deployments is virtually always a complex engineering project where the most significant expenses are often hidden. Scoping and committing to a Kafka migration requires multiple layers of careful calculation involving infrastructure choices, data complexity, team expertise, and risk tolerance. Underestimating these variables leads to blown budgets and extended timelines.

If you’re on a platform team running Apache Kafka, you know it’s rarely simple. You’re expected to keep it stable, performant, and secure while juggling requests from every direction. Supporting multiple teams and partners leads to operational complexity that never really goes away.

Data-driven organizations know that unlocking real-time analytics from streaming data isn’t just about collecting and transmitting events. It’s about getting high-quality, governed, and query-ready tables into the hands of analysts and business users while ensuring enterprise-grade security and compliance. Traditionally, moving data from Apache Kafka into analytic tables required complex ETL pipelines, manual data wrangling, and custom governance processes.

If you’re running Confluent Platform or our new offering, Confluent Private Cloud, on-premises, you have your reasons: data sovereignty, regulatory compliance, or maybe a phased cloud migration. Your on-prem Apache Kafka isn’t going anywhere. It’s a critical part of your infrastructure.

The most innovative companies know that artificial intelligence (AI) problems are fundamentally data problems. To produce the right results, AI applications, ranging from real-time fraud detection to autonomous agents, must share a common foundation: trustworthy data in motion.

The true power of data emerges when streaming, analytics, and artificial intelligence (AI) connect—transforming real-time streaming data into actionable intelligence. Yet bridging that gap has long been one of the most complex challenges in modern data architecture. Confluent makes it effortless to capture and process continuous streams of data, while Databricks empowers teams to analyze, govern, and apply AI through Unity Catalog.