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The standard approach for Kafka in enterprise environments is to lock it down and have engineers file tickets. Platform teams do that because traditional engineering tools (CLIs, UIs and native ACLs) have no enterprise governance, so you cannot grant access you can scope, revoke or prove later. That’s the Kafka ticket desk: the week is access requests instead of platform work, engineers wait days to ship and Kafka adoption scales only as fast as the ticket queue moves.

What's new in Q3'26: Real-time anomaly detection and forecasting with IBM Granite Time Series models and Google TimesFM model, upserts for Real-Time Context Engine to maintain fresh context for AI, and lightning queries to instantly serve the current state of the business for operational apps, analytics, and more.

Perform robust real-time forecasting on your Kafka data streams using IBM Granite Time Series models TTM, FlowState, PatchTST-FM right in Flink. Get started easily with zero-config, model flexibility, and lower cost in fully managed Confluent Cloud.

Fully managed Confluent Cloud for Apache Flink combines Flink SQL for data engineers with programmatic capabilities like the Table API, User-Defined Functions (UDFs), and Process Table Functions (PTFs) for developers. You can build mission-critical use cases with programmatic capabilities while using familiar dbt- and SQL-based workflows.

Most Kafka war rooms don't start at 2am in production. They start weeks earlier, when an engineer couldn’t explore a topic, see partition distribution, a quota or a config and shipped hoping for the best. That's the Platform Engineering waiting room: developers building apps waiting in a queue of tickets, with no self-service visibility into Kafka. That release triggers a P1 later.