Confluent Blog

Kafka is your event backbone, not your inference runtime. This guide breaks down three patterns for running AI alongside Kafka (external API, embedded, sidecar), when to use each, and how to handle topic design, dead-letter queues, idempotency, and LLM cost control.

Batch ETL feeds AI models data that's hours old. That causes context drift in RAG, training-serving skew in fraud detection, and broken operational AI. This guide covers the Ingest, Process, Serve architecture using Kafka and Flink to keep embeddings, features, and context fresh in milliseconds.

Unstructured data (PDFs, scans, images) breaks every assumption built for structured pipelines. This guide walks through a four-stage streaming architecture for turning messy binary blobs into RAG-ready chunks and embeddings, with patterns for rate limits, cost control, and fault tolerance.

Stream processing and real-time OLAP solve different problems, but vendor marketing makes them sound the same. This guide breaks down when to use Flink vs ClickHouse/Pinot, what to precompute vs query on the fly, and how Kafka connects both layers into one architecture.

This blog post introduces KCP integration with Gateway, which automates Kafka client cutover by routing traffic through an auth-translating proxy and orchestrating group-based, offset-safe migrations to Confluent Cloud with just a few CLI commands.

We’re excited to announce Confluent Platform 8.2! Built on Apache Kafka 4.2, it introduces Queues for Kafka (GA) for native task-queue workloads, Flink SQL (GA) to simplify stream processing, and CPC Gateway 1.2 for seamless client switchover to make managing migrations easier than ever.

Agent Taskflow built a multi‑agent AI platform on Confluent Cloud and AWS that can scale from one to one million agents with sub‑30ms latency. This post breaks down their architecture, benchmark results, and why an event‑driven backbone is critical for production agentic AI.

CISOs are shifting from expensive, locked-in SIEMs to Open Security Lakes using Apache Iceberg. Confluent powers this decoupled architecture by filtering data in real-time, slashing ingestion costs, breaking vendor lock-in, and enabling faster threat detection.

Batch ELT pipelines create duplication, cost spikes, and governance gaps as data scales. Here’s why enterprises are rethinking legacy integration models.

Learn how to add your first ML model to a real-time streaming pipeline. Learn a simple, low-risk pattern for inference, scoring, and deployment with Apache Kafka®.

Confluent Cloud for Government has achieved FedRAMP Moderate authorization, allowing federal, state, and local agencies to deploy secure, fully managed data streaming. By eliminating the operational burden of self-managed Kafka, the platform helps agencies break down data silos, modernize legacy...

Confluent’s Schema IDs in headers transform Kafka from "dumb pipes" to a "smart data plane." By moving metadata out of payloads, teams can schematize topics without breaking legacy apps or requiring big-bang migrations. This unlocks governed, AI-ready data for Flink and lakehouses with ease.

Design energy-efficient, low-cost streaming systems. Learn GreenOps patterns to reduce compute waste, optimize storage, and lower the carbon footprint of real-time data.

At Confluent, our mission is to provide the world’s most secure and scalable data streaming platform. As cryptographic standards evolve to meet the challenges of the future, we are committed to ensuring your data remains protected against emerging threats—including the eventual development of Cr...