Systems | Development | Analytics | API | Testing

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.

Your capacity management has to go beyond ideal scenarios. It isn’t just about handling peak traffic, it’s about the failover and the failback. OCBC's George Goh explains why continuous monitoring is the only way to ensure your system is ready for the unexpected tomorrow.

OCBC migrated thousands of microservices, batch jobs, and monolithic applications across multiple regions in just six hours with zero data loss. George Goh shares how six months of planning enabled flawless execution.

Migrating an entire bank's infrastructure without losing a single transaction sounds impossible. Yet, OCBC successfully modernized their event-driven architecture across Singapore, Malaysia, and Hong Kong with zero data loss. In this episode, George Goh (Executive Director at OCBC) joins Joseph Morais and Sami Amed (Staff Solutions Engineer at Confluent) to pull back the curtain on a massive data streaming migration, how the team executed a flawless migration of thousands of producers and consumers in a single weekend.

Use Real-Time Context Engine and Claude, or any MCP-compatible client, to explore operational data using natural language in real time. That includes everything from simple lookups to multi-step investigative questions like: Confluent’s Real-Time Context Engine gives AI agents live access to operational context as events happen across the business. Instead of relying on stale snapshots, agents can query and reason over continuously updated tables in real time.