Confluent Blog

Confluent Private Cloud matches Kafka performance with up to 73% fewer brokers. Benchmarks prove massive TCO savings, while Centralized Policy Enforcement streamlines data governance and broker-native multi-tenancy provides the isolation needed to end "noisy neighbor" issues for good.

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’s Q2 ‘26 launch makes AI-ready streaming accessible by delivering SQL-based workflows via the dbt adapter for Flink, enhanced developer tooling with managed MCP server and Agent Skills, production-grade AI solutions through Streaming Agents and Real-Time Context Engine, and more.

Explore new Confluent Intelligence features: enhanced querying with Real-Time Context Engine, PII detection, sentiment analysis, and support for TimesFM, Anthropic, and Fireworks AI models.

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 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 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.

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.