The Pulse
Confluent Makes Lightning Tables Generally Available on AWS, Adds Query as Fifth Platform Pillar
Confluent announced new capabilities for building a Streamhouse, an architecture that keeps business context current for AI agents, applications and analytics. Lightning Tables is generally available on AWS, and Query is the fifth pillar of
AI.info Team · Written with AI · fact-checked ·
In Confluent’s illustrative airline example, a canceled flight leaves operations, scheduling and reservations with conflicting views of aircraft location, departure time and available seats. On November 4, 2026, Confluent announced new capabilities for building a Streamhouse, a new type of data architecture that keeps business context current for AI agents, applications and analytics. Lightning Tables is now generally available on AWS, and Query is now the fifth pillar of Confluent’s Data Streaming Platform.
Why enterprises need a Streamhouse
In the example, flight operations track where the aircraft is, scheduling tracks when it will depart, and reservations tracks which seats are available. When those systems update at different times, an autonomous rebooking agent can assemble an itinerary that looks valid in each system but does not work in practice: a seat is available, but the aircraft cannot arrive in time for the passenger’s connection. Customer-service teams and notification systems can then act on those same conflicting views, spreading the disruption.
With a Streamhouse, each change can update a shared picture of the airline’s operations. The airline’s source systems send updates about aircraft location, departure delays and seat bookings into a continuous flow of events. A Streamhouse combines those updates into current business context and makes it available to rebooking agents, customer-service teams and applications as they work, so they can check aircraft location, departure time and seat availability together before offering a passenger a new itinerary.
Confluent’s Data Streaming Platform Now Offers a Complete Streamhouse
A Streamhouse continuously captures, processes, governs and serves current business context. Its three principles are current, mission-critical and decentralized: it maintains continuously updated business context, is engineered to production service levels, and meets data where it lives without requiring consolidation first. The architecture works alongside existing data warehouses and lakehouses, adding a real-time operational layer for decisions and actions.
Confluent’s Data Streaming Platform provides a complete, unified foundation for building a Streamhouse and brings together Stream, Connect, Process, Govern and Query. Apache Kafka captures and streams business events; Connect brings data from enterprise systems into Kafka and delivers it downstream; and Apache Flink combines, enriches and derives business context close to the source as data changes. Governance capabilities help teams discover, classify and secure data through lineage, catalog, quality, encryption, schema and access-control capabilities.
The Query pillar serves current business context to analytics, agents and applications through the interface that fits the job. Confluent Tableflow makes streaming data available to lakehouses and warehouses as open table formats. The Real-Time Context Engine serves current business context to AI agents through the Model Context Protocol (MCP), while Lightning Tables gives applications low-latency SQL and API access to the current state of the business.
Lightning Tables: Fast access to the current state of the business
Generally available on AWS, Lightning Tables on Confluent Cloud is a read-only serving layer that durably stores and materializes Apache Kafka data into tables, keeping them current as changes arise. It makes the current state of the business available for low-latency queries through SQL and a direct API. It also applies schemas, lineage and access policies from data capture through serving, and manages schema changes to help prevent breaks in downstream applications.
In Confluent internal testing, approximately 100 milliseconds elapsed from new data creation to serving, with query latency of 10-100 milliseconds and throughput of 1,000+ queries per second. Confluent presents the managed serving layer as a way to serve continuously without separate ETL pipelines, databases or caches. It also says teams can compute features in Flink and store them for machine learning use cases, including real-time personalization.
Accenture uses a Streamhouse architecture and Extend describes its architecture
Accenture uses a Streamhouse architecture powered by Confluent to connect data across its global business. Thomas Mahaffey, SAI Chief Data Strategist at Accenture, describes moving to a Streamhouse architecture powered by Confluent as decoupling data producers from consumers and transforming high-volume event streams into governed, reusable real-time data products. He says the continuous data foundation dropped Accenture’s time-to-insight from months to days and slashed reporting times, empowering Accenture’s teams to focus on delivering high-value AI for its 800,000 employees.
Extend describes its architecture as a 100% serverless Streamhouse anchored by Confluent Cloud. Jonathan Kropp, Director of AI and Architecture at Extend, says the company uses Kafka as its operational source of truth, with downstream microservices consuming and querying real-time data. Kropp says the architecture provides the flexibility and responsiveness required to scale operations, adapt to live market signals and power real-time agentic AI workflows.