Confluent, an IBM company, has announced new capabilities for building a Streamhouse, a data architecture designed to keep business context current for AI agents, applications, and analytics as they make decisions and take action.
Greg Taylor, SVP & general manager, APAC, Confluent, said: “A Streamhouse gives teams a continuously updated, governed foundation for that context, so applications and agents can work from the same current picture. It helps organisations build on their existing data investments as they scale to bring more responsive, real-world AI experiences into operation.”
Streamhouse
Confluent states that Streamhouse continuously captures, processes, governs, and serves current business context across agents, applications, and analytics.
Streamhouse is built on three architectural principles: current, mission-critical, and decentralised. It maintains continuously updated business context, is engineered for production service levels, and accesses data where it resides without requiring prior consolidation. It integrates with existing data warehouses and lakehouses, adding a real-time operational layer for decisions and actions.
Confluent’s Data Streaming Platform (DSP) offers a unified foundation for building a Streamhouse. At its core is Apache Kafka, which captures and streams business events.
Lighting Tables on Confluent Cloud
Now generally available on AWS, Lightning Tables on Confluent Cloud is a read-only serving layer that durably stores and materialises Apache Kafka data into tables, keeping them current as changes occur. It provides the current state of the business for low-latency queries.
“This continuous data foundation has dropped our time-to-insight from months to days and slashed reporting times, empowering our teams to focus on delivering high-value AI for our 800,000 employees,” said Thomas Mahaffey, SAI chief data strategist, Accenture.
