Confluent Blog

InfiniteWatch is building an AI-native customer interaction intelligence platform that unifies these streams into a continuous, real-time understanding of customer behavior and operational state. At its core is Confluent.

Confluent's architectural approach to digital sovereignty for real-time streaming, showcasing our deployment spectrum, zero-access guarantees, and open-source foundation

Confluent Tableflow simplifies the process of feeding data lakes and lakehouses by turning Kafka topics directly into analytics-ready Iceberg or Delta Lake tables, eliminating complex traditional ETL stacks leading to 30%–50% lower total ingestion costs.

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 Cloud rolled out new observability updates that give operators direct visibility into streaming workload performance. New Metrics API signals expose client throttling by principal, consumer group rebalance duration, connection attempt spikes, and compacted partition counts.

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.

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.