Technology

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.

Before deploying agentic AI, enterprises should be prepared to address several issues that could impact the trustworthiness and security of the system.

Learn how an e-commerce company integrates the data from its Stripe system with the Pinecone vector database using the new fully managed HTTP Source V2 and HTTP Sink V2 Connectors along with Flink AI model inference in Confluent Cloud to enhance its real-time fraud detection.

Confluent Cloud Freight clusters are now Generally Available on AWS. In this blog, learn how Freight clusters can save you up to 90% at GBps+ scale.

Confluent's advanced security and connectivity features allow you to protect your data and innovate confidently. Features like Mutual TLS (mTLS), Private Link for Schema Registry, and Private Link for Flink, not only bolster security but also streamline network architecture and improve performance.

Confluent’s Create Embeddings Action for Flink helps you generate vector embeddings from real-time data to create a live semantic layer for your AI workflows.

The rise of agentic AI has fueled excitement around agents that autonomously perform tasks, make recommendations, and execute complex workflows. This blog post details the design and architecture of PodPrep AI, an AI-powered research assistant that helps the author prepare for podcast interviews.

Confluent Cloud 2024 Q4 adds private networking and mTLS authentication, follower fetching, Flink updates, WarpStream features to support migration and governance, and more!

GenAI thrives on real-time contextual data: In a modern system, LLMs should be designed to engage, synthesize, and contribute, rather than to simply serve as queryable data stores.

Continuing issues with hallucinations, the increasing independence of agentic AI systems, and the greater usage of dynamic data sources, are three AI trends you may want to monitor in 2025.

In this final part of the blog series, we bring it all together by exploring data streaming platforms (DSPs), event-driven architecture (EDA), and real-time data processing to scale AI-powered solutions across your organization.

In Part 2 of the series, we take things a step further by enhancing GenAI with the tools it needs to deliver smarter, more relevant responses. We introduce retrieval-augmented generation (RAG) and vector databases (VectorDBs), key technologies that provide LLMs with the context they need.

This blog series explores how technologies like generative AI, RAG, VectorDBs, and DSPs can work together to provide the freshest and most actionable data. Part 1 lays the foundation for understanding how data fuels AI, and why having the right data at the right time is essential for success.

Discover how predictive analytics, powered by generative AI and data streaming, transforms business decisions with real-time insights, accurate forecasts, and innovation.