Technology

We’re excited to announce that Confluent for VS Code is now Generally Available with Confluent Cloud and Confluent Platform! This integration with Visual Studio Code is open source, readily accessible on the VS Code Marketplace, and supports all forms of Apache Kafka® deployments.

Confluent Cloud Q1 ’25 introduces Tableflow, Freight clusters, and Flink AI enhancements

This blog announces the general availability of Confluent Platform 7.9 and its latest key features: Oracle XStream CDC Connector, Client-Side Field Level Encryption (EA), Confluent for VS Code, and more.

Tableflow represents Kafka topics as Apache Iceberg® (GA) and Delta Lake (EA) tables in a few clicks to feed any data warehouse, data lake, or analytics engine of your choice

Confluent's new Oracle XStream CDC Premium Connector delivers enterprise-grade performance with 2-3x throughput improvement over traditional approaches, eliminates costly Oracle GoldenGate licensing requirements, and seamlessly integrates with 120+ connectors...

Announcing the release of Apache Kafka 4.0.

Learn how to delete topics in Apache Kafka safely and efficiently. Explore step-by-step instructions, best practices, and important considerations for managing Kafka topics.

Discover how Confluent’s BYOK for Enterprise Clusters and support for EKMs enhance security, and efficient data streaming in our latest blog.

Introduction to Flink SQL FEDERATED_SEARCH() on Confluent cloud. FEDERATED_SEARCH() along with ML_PREDICT() enables developers to execute GenAI use cases with data streaming technologies.

Confluent Cloud now supports Cluster Linking for Azure Private Link, enabling secure data replication between Kafka clusters in private Azure environments. With Cluster Linking, organizations can achieve real-time data movement, disaster recovery, migrations, and secure data sharing

Most AI projects fail not because of bad models, but because of bad data. Siloed, stale, and locked in batch pipelines, enterprise data isn’t AI-ready. This post breaks down the data liberation problem and how streaming solves it—freeing real-time data so AI can actually deliver value.

The concept of “shift left” in building data pipelines involves applying stream governance close to the source of events. Let’s discuss some tools (like Terraform and Gradle) and practices used by data streaming engineers to build and maintain those data contracts.

This article explores how event-driven design—a proven approach in microservices—can address the chaos, creating scalable, efficient multi-agent systems. If you’re leading teams toward the future of AI, understanding these patterns is critical. We’ll demonstrate how they can be implemented.

Learn how Flink enables developers to connect real-time data to external models through remote inference, enabling seamless coordination between data processing and AI/ML workflows.