Confluent Blog
Queues for Apache Kafka® Is Here: Your Guide to Getting Started in Confluent
Confluent announces the General Availability of Queues for Kafka on Confluent Cloud and Confluent Platform with Apache Kafka 4.2. This production-ready feature brings native queue semantics to Kafka through KIP-932, enabling organizations to consolidate streaming and queuing infrastructure while...
AI Tools for Builders — Confluent's MCP Server & Agent Skills
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.
Confluent Intelligenceの新機能：Real-Time Context Engineのアップグレードや新しいモデルのサポート、ML関数など
Real-Time Context Engineによるクエリ機能の強化、PII検出、センチメント分析、およびTimesFM、Anthropic、Fireworks AIの各モデルのサポートなど、Confluent Intelligenceの新機能をご紹介します。
Simplify Real-Time Context Engineering for Snowflake Intelligence With Confluent
Power Snowflake Cortex agents with real-time, trustworthy context using Confluent Intelligence, Streaming Agents, and the Real-Time Context Engine.
IBM to Acquire Confluent
We are excited to announce that Confluent has entered into a definitive agreement to be acquired by IBM.
Empowering the Data Streaming Ecosystem: Evolving Confluent Hub to Confluent Marketplace
Confluent Hub has evolved into the new Confluent Marketplace—a curated catalog when you can discover, purchase, and deploy community and partner-built integrations and streaming solutions.
Connecting the Dots: Simplifying Multi-API Data Flows into Apache Kafka®
This blog introduces the concept of API chaining — a method where data is collected by sequentially calling multiple related APIs. The response from one API is used to construct the request for the next, creating a chain that enables richer, more contextual data collection.
Confluent が「2025 年第 4 四半期のForrester Wave™: ストリーミングデータプラットフォーム」でリーダー企業に選出
このブログ記事では、Forrester Researchが『The Forrester Wave™: Streaming Data Platforms, Q4 2025』においてConfluentをリーダーに選定した経緯、Confluentデータストリーミングプラットフォームの価値とメリットについて解説します。
The Secret to Scaling Data Streaming at BMW Group
Scale data streaming within your organization by building an internal community. Align leaders, engage teams, and sustain adoption—featuring BMW Group’s blueprint and outcomes—to accelerate ROI and keep your platform relevant.
Confluent Expands in Brazil to Help More Companies Harness Real-Time Data
Learn why Confluent is expanding its team in Brazil so customers like iFood & Stone Pagamentos can use real-time data to modernize and out-innovate the competition.
Data Streaming Platforms: The Cornerstone of Enterprise AI
Discover how a data streaming platform helps you unlock the full potential of your AI—and translates it into measurable business value.
How to Monetize Enterprise Data: The Definitive Guide
Learn how to design and implement a real-time data monetization engine using streaming systems. Get architecture patterns, code examples, tradeoffs, and best practices for billing usage-based data products.
Detecting the Unexpected: Built-in Real-Time Anomaly Detection With Confluent Cloud for Apache Flink®
Learn how the built-in anomaly detection ML function in Confluent Cloud for Apache Flink® enables event-driven AI agents to detect and act on outlier system events faster.
Context-Driven AI Reigned Supreme at Current New Orleans
Catch up on Current New Orleans—and how executives, architects, developers, and data engineers alike learned about why AI should be powered by event-driven, context-rich data streams.
How Superwall Uses WarpStream and ClickHouse Cloud to Scale Subscription Monetization
Learn why Superwall chose to scale its subscription monetization business model with Warpstream and Clickhouse Cloud.
Real-Time Context Engineのご紹介: AI 向けリアルタイム処理データによって簡素化されるコンテキストエンジニアリング
Real-Time Context EngineでAIのリアルタイムコンテキストを解き放つ。Confluent Cloudで信頼性の高いコンテキストを継続的に評価、処理、提供。
より速く、よりスマートに、よりコンテキストアウェアに: Streaming Agentsの新機能
Streaming Agentsの初めての発表はお客様の根本的な課題の解決につながりました。実はそれは、あらゆる AI の問題はデータの問題であるということです。