Systems | Development | Analytics | API | Testing

New in Confluent Cloud and WarpStream: Evolving the Data Streaming Platform for AI, Scale, and Control

In the last few months, Confluent has released over 70 new features to our data streaming platform, ranging from new connectors to better networking connectivity to entirely new ways to build streaming applications. We are not slowing down. Confluent’s DSP can do more, across every pillar, in ways that are more powerful, more complete, and more connected than ever before.

New in Confluent Intelligence and AI Tools: Making Agents Native to the Stream, Expanded Model Support, New Agent Skills, and Copilot

A customer writes in asking where their order is. The AI support agent checks the account, sees the order marked shipped, and sends a reply. The order was cancelled forty minutes ago. The agent wasn't wrong about anything it could see. It was reasoning over stale data that refreshes every six hours. This is why AI projects stall. Not because the models aren't capable, but because they lack AI-ready data and a reliable view of the current state of the business.

Confluent Cloud for Apache Flink: Engine for Mission-Critical, Real-Time Operational Systems and dbt/SQL-Native Home for Data Science and AI

Organizations today are under immense pressure to deliver on two critical fronts: building mission-critical, real-time operational systems and powering the next generation data science and artificial intelligence (AI) workflows with analytics-ready data. Historically, achieving both meant navigating a divided, complex architecture.

Who's Responsible When AI Gives You the Wrong Answer? Andy Cotgreave and Francois Lopitaux Debate

If an AI agent gives a business user the wrong number, and a consequential decision gets made off it, who's responsible? The data analyst who built the semantic layer? The platform? Or the business user who asked the question and acted on the answer?

Real-Time Fraud Detection with Edge-to-AI | Cloudera Data in Motion Demo

Learn how to build an end-to-end, real-time edge-to-AI data pipeline to tackle critical enterprise challenges like credit card fraud detection. In this demo, Diby Malakar (Product Lead for Data in Motion) demonstrates how to process an average of 5,000 transactions per second in low hundreds of milliseconds to detect fraud instantly. Discover how Cloudera’s Data in Motion suite enables application developers to ingest, govern, and enrich streaming edge data to power instant AI model inference and live analytics.

Connect AI Agents with MCP | Do It Better with FME & Snowflake

See how AI agents use Model Context Protocol (MCP) with FME to connect complex enterprise and spatial data to Snowflake. In this episode of Do It Better with Snowflake, Safe Software CEO Don Murray demonstrates how FME extends Snowflake Cortex AI Agents with FME workflows for data integration, transformation, and spatial analysis. See how Snowflake + FME can help you: Connect complex data: including GIS, CAD, 3D, LiDAR, BIM, ERP, and unstructured data.

Live Workshop: Why Your ELT Bill Keeps Growing and How to Fix It

See why your ELT bill keeps climbing. If you're wondering why your ELT bill keeps growing, this workshop is for you! In this live workshop, we break down ELT pricing, MAR pricing, data pipeline costs, and the hidden costs of data integration so you can understand what you're actually paying for. If you're evaluating Fivetran, Airbyte, Hevo, or other ELT platforms, this workshop shows how different pricing models work and what can make your bill harder to predict as your data grows.