Technology

Confluent's fully-managed IBM MQ Source and Sink connectors eliminate the operational overhead of self-managed Kafka Connect, securely bridging IBM MQ's transactional reliability with modern event streaming platforms like Kafka.

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.

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.

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.

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.

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’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...

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...

Learn how real-time decisioning and autonomous data systems enable organizations to act on data instantly using streaming, automation, and AI.