Systems | Development | Analytics | API | Testing

Tableflow is Production Ready: Delta Lake, Unity Catalog, Azure Early Availability (EA), and More Enterprise-Grade Features

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.

Unified Stream Manager: Manage and Monitor Apache Kafka Across Environments

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.

Streaming Data to AI-Ready Tables: Tableflow for Delta Lake and Databricks Unity Catalog Is Now Generally Available

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.

Faster, Smarter, More Context-Aware: What's New in Streaming Agents

When we first introduced Streaming Agents, we were solving a fundamental challenge: Every AI problem is a data problem. When data is missing, stale, or inaccessible, even the most advanced agents and LLMs fail to deliver. How do we build scalable agents that aren’t just powerful in isolation, but part of multi-agent systems that are event-driven, replayable, and grounded in accurate data?

Introducing Real-Time Context Engine: Simplified Context Engineering With Real-Time, Processed Data for AI

We’re excited to announce our Real-Time Context Engine, now available in Early Access. It’s a key part of Confluent Intelligence, our vision to bring real-time data directly to production AI systems through the power of Apache Kafka and Apache Flink.

Demo: Streaming Agents for price matching, with RAG, observability, and Real-Time Context Engine

Streaming Agents enable you to build, deploy, and orchestrate event-driven agents on Apache Flink and Apache Kafka. Embedded in the stream, they can tap into the latest enriched data and be the eyes and ears of a business, continuously monitoring and acting on live operational events. In this demo, Brenner Heintz, Staff Technical Marketing Manager at Confluent, shows how to build price matching agents, do vector search for retrieval augmented generation (RAG), and leverage Confluent’s Real-Time Context Engine to process and serve fresh context the moment it’s needed for AI decision-making.

Confluent and Your Data: A Partnership You Can Trust

At Confluent, we know that our platform must provide your business with resilience for your mission-critical applications, and we take that responsibility very seriously. Any unplanned outages can result in lost revenue, reputation damage, or fines. As incidents inevitably happen, your organization needs to know how to maximize your availability with our products.

The True Cost of Real-Time Data Streaming

Thanks to ever-increasing adoption technologies like Apache Kafka and Apache Flink, the continuous movement and streaming of real-time data has transformed how modern businesses operate… but is the cost of data streaming worth it? From powering personalized recommendations to enabling instant fraud detection, streaming is often seen as synonymous with innovation and competitive advantage. But like any investment, the cost-benefit equation has to make sense.

How to Build Real-Time Compliance & Audit Logging With Apache Kafka

Traditionally, compliance teams have had to rely on batch exports for their audit logs, a method that, while functional, is proving to be woefully inadequate in today's fast-paced digital landscape. The truth is, waiting hours, or even days, for batch exports of your audit data leaves your organization vulnerable.