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Confluent Blog
Data Products, Data Contracts, and Change Data Capture
Change data capture is a popular method to connect database tables to data streams, but it comes with drawbacks. The next evolution of the CDC pattern, first-class data products, provide resilient pipelines that support both real-time and batch processing while isolating upstream systems...
Unlock Cost Savings with Freight Clusters–Now in General Availability
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.
Unleash Real-Time Agentic AI: Introducing Streaming Agents on Confluent Cloud
Build event-driven agents on Apache Flink® with Streaming Agents on Confluent Cloud—fresh context, MCP tool calling, real-time embeddings, and enterprise governance.
Agentic AI: The Top 5 Challenges and How to Overcome Them
Before deploying agentic AI, enterprises should be prepared to address several issues that could impact the trustworthiness and security of the system.
Introducing Real-Time Embeddings: Any Model, Any Vector Database—No Code Needed
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.
Automating Podcast Promotion with AI and Event-Driven Design
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.
Revolutionizing Failure Management in Apache Flink: Meet FLIP-304's Pluggable Failure Enrichers
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.
Confluent Champion Maria on the Power of a Motivated Workplace
In our latest Confluent Champion, staff solutions engineer Maria Berinde-Tampanariu shares how Confluent fosters a culture of motivation and growth.
Using Apache Flink® for Model Inference: A Guide for Real-Time AI Applications
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.
Allium and Confluent: How to Build a Foundational Data Platform for Blockchain
Allium provides real-time, accessible blockchain data for analytics and business teams with the help of data streaming. Learn how here.
Optimizing Supply Chains with Data Streaming and Generative AI
Effective supply chain management relies on the ready availability of well-governed, real-time data. Learn how Confluent facilitates supply chain optimization.
Event-Driven AI: Building a Research Assistant with Kafka and Flink
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.
New in Confluent Cloud: Extending Private Networking Across the Data Streaming Platform
Confluent Cloud 2024 Q4 adds private networking and mTLS authentication, follower fetching, Flink updates, WarpStream features to support migration and governance, and more!
IAG & Kmart: The Business Impact of Data Streaming in Australia & New Zealand
Discover how Confluent has transformed data management for Kmart and IAG in Australia and New Zealand with its real-time data streaming platform.
Stop Treating Your LLM Like a Database
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.
Scaling Web Scraping with Data Streaming, Agentic AI, and GenAI
In building the next generation of web agents, we need the simplest, fastest way to extract web data at scale for production use cases.
Three AI Trends Developers Need to Know in 2025
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.