Systems | Development | Analytics | API | Testing

Confluent Recognized as a Leader in The Forrester Wave: Streaming Data Platforms, Q4 2025

As artificial intelligence (AI) adoption has grown over the last several years, it has become increasingly clear to companies and observers that successful AI requires real-time data and context. And it’s not just AI—every critical business function that relies on data inputs needs that data to be as trustworthy and fresh as possible.

LIVE from Current NOLA: Scaling Streaming in the AI Era | Life Is But A Stream

From the heart of the expo hall at Current NOLA, this special episode drops you into the conversations, energy, and the breakthroughs as they happened. Host, Joseph Morais, and co-host, Adi Polak talk with data streaming leaders and community voices to unpack how teams are using Apache Kafka and Apache Flink to power low-latency, AI-ready applications—covering patterns from usage-based billing and hybrid operations to cost efficiency and streaming governance. You’ll also hear how shift-left processing and governance make AI-ready data possible at scale.

How to Monetize Enterprise Data: The Definitive Guide

Most modern enterprises are generating more data than they can use, let alone integrate, analyze, and extract value from. Some figure out the secret to data value extraction is monetization—identifying data so valuable that other companies will pay for it. And now, the shift to real time data monetization is no longer a strategic option but a fundamental requirement for modern enterprises.

This Is Where Agentic AI Is Headed Next | Insights From Current NOLA

Ever wondered what's next for agentic AI? In this quick chat, we dive into how agentic AI and real-time data are shaping the future—helping systems predict trends, make smarter decisions, and much more. Watch to find out what could be coming in 2026.

The Future of Data Is All in the Packaging | Current '25 Keynote Highlights

First, Simon Aubury, co-author of "Getting Started With DuckDB," explains why treating data as a product (complete with an owner, a schema, and SLAs) is the key to building a culture of quality and accountability. He explores how this shift in mindset creates the right incentives for application developers and aligns the entire organization on the true value of its data.