New industry initiative establishes an open category for data architectures that power real-time applications and AI agents
Learn how to migrate to Confluent Cloud in hours using Confluent’s open source Kafka Copy Paste tool. Get an in-depth introduction to the KCP tool and a walk-through of the four steps of migrating from MSK to Confluent Cloud using the tool.
Confluent's AI developer tools are now GA: an open-source local MCP server, a managed MCP server, and Agent Skills. Together they give AI coding assistants direct access to your streaming platform — the tools to act on it and the domain knowledge to build correctly.
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...
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 VECTOR_SEARCH() on Confluent cloud. VECTOR_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.
Learn how to use the recently launched Provisioned Mode for Lambda’s Kafka ESM to build high throughput Kafka applications with Confluent Cloud’s Kafka platform. This blog also exhibits a sample scenario to activate and test the Provisioned Mode for ESM, and outline best practices.
We built an AI-powered tool to automate LinkedIn post creation for podcasts, using Kafka, Flink, and OpenAI models. With an event-driven design, it’s scalable, modular, and future-proof. Learn how this system works and explore the code on GitHub in our latest blog.
FLIP 304 lets you customize and enrich your Flink failure messaging: Assign types to failures, emit custom metrics per type, and expose your failure data to other tools.