Data modeling is easier with coding assistants that actually understand the projects they’re working on.

Organizations today are under immense pressure to deliver on two critical fronts: building mission-critical, real-time operational systems and powering the next generation data science and artificial intelligence (AI) workflows with analytics-ready data. Historically, achieving both meant navigating a divided, complex architecture.

A customer writes in asking where their order is. The AI support agent checks the account, sees the order marked shipped, and sends a reply. The order was cancelled forty minutes ago. The agent wasn't wrong about anything it could see. It was reasoning over stale data that refreshes every six hours. This is why AI projects stall. Not because the models aren't capable, but because they lack AI-ready data and a reliable view of the current state of the business.

In the last few months, Confluent has released over 70 new features to our data streaming platform, ranging from new connectors to better networking connectivity to entirely new ways to build streaming applications. We are not slowing down. Confluent’s DSP can do more, across every pillar, in ways that are more powerful, more complete, and more connected than ever before.

Your data engineer just accidentally deleted the production customer table. Again. This scenario plays out in organizations worldwide, not because of malicious intent, but because of poorly configured access controls. With data breaches and compliance violations, managing who can access what in your Snowflake data warehouse isn't optional: it's essential.