Apache Flink

Learn how the built-in anomaly detection ML function in Confluent Cloud for Apache Flink® enables event-driven AI agents to detect and act on outlier system events faster.

Explore new Streaming Agents features — Agent Definition, Observability & Debugging, and access to Real-Time Context Engine — to build intelligent, context-aware AI on Confluent.

Build event-driven agents on Apache Flink® with Streaming Agents on Confluent Cloud—fresh context, MCP tool calling, real-time embeddings, and enterprise governance.

This blog post demonstrates building streaming ETL pipelines with Confluent Cloud for Apache Flink using SQL to transform and enrich UK flood monitoring data. It shows how to unpack nested JSON arrays, join multiple data sources, and use Tableflow to expose the enriched data as Iceberg tables for...

Explore how Flink Agents redefine enterprise AI with real-time data, event-driven processing, and scalable autonomy—built for the future of AI workflows.

Just as some problems are too big for one person to solve, some tasks are too complex for a single artificial intelligence (AI) agent to handle. Instead, the best approach is to decompose problems into smaller, specialized units so that multiple agents can work together as a team.

By combining Google A2A’s structured protocol with Kafka’s powerful event streaming capabilities, we can shift from brittle, point-to-point integrations to a dynamic ecosystem where agents publish insights, subscribe to context, and coordinate in real time. Observability, auditability, and...

An Apache Flink® job not producing results often indicates an issue with watermarks, which are necessary for handling out-of-order message processing in time-based aggregations from Kafka. Watermarks balance data loss and latency by defining how long to wait for late messages. Incorrectly setting...

Salesforce has Agentforce, Google launched Agentspace, and Snowflake recently announced Cortex Agents. But there’s a problem: They don’t talk to each other…

Introduction to Flink SQL VECTOR_SEARCH() on Confluent cloud. VECTOR_SEARCH() along with ML_PREDICT() enables developers to execute GenAI use cases with data streaming technologies.

Learn how Flink enables developers to connect real-time data to external models through remote inference, enabling seamless coordination between data processing and AI/ML workflows.

FLIP 304 lets you customize and enrich your Flink failure messaging: Assign types to failures, emit custom metrics per type, and expose your failure data to other tools.

Confluent’s Create Embeddings Action for Flink helps you generate vector embeddings from real-time data to create a live semantic layer for your AI workflows.