Aiven, Confluent, Redpanda, StreamNative and Ververica Form Streamhouse Working Group

New industry initiative establishes an open category for data architectures that power real-time applications and AI agents

HELSINKI, Finland; MOUNTAIN VIEW, Calif.; SAN FRANCISCO; and MUNICH, Germany, Sept. 15, 2026 — Aiven, Confluent, Redpanda, StreamNative and Ververica today announced the formation of the Streamhouse Working Group and published an open, vendor-neutral definition of Streamhouse, a data architecture designed to keep the current state of a business continuously available to production applications, analytics, and AI agents.

Under the binding Trademark Commitment published with the definition, anyone may use the Streamhouse name to refer to the category or architecture, permanently and free of charge.

A Shared Data Architecture for the Age of AI

Organizations are beginning to deploy applications and AI agents that do more than analyze what happened in the past: they make decisions and take action as business events unfold. To act reliably, these systems need fresh, governed business context delivered continuously and at production service levels.

The business context these applications and agents need is distributed across databases, applications, cloud services, and analytical systems. The challenge is making that context available wherever it is needed, with the freshness, governance, and reliability that production use requires.

The five founding companies approach this challenge from different technological perspectives. They formed the Streamhouse Working Group because they see organizations converging on a common architectural pattern. Streaming technologies are foundational to that pattern, but no single project, component, or vendor portfolio constitutes a Streamhouse on its own.

Defining the Streamhouse Architecture

A Streamhouse architecture enables organizations to capture, transport, transform, govern, and serve the current state of their business continuously so that production applications and AI agents can act on it.

The architecture is defined by three attributes:

• Real-time: Data remains continuously current as business events occur, rather than being refreshed only through periodic batch processes.

Real-time: Data remains continuously current as business events occur, rather than being refreshed only through periodic batch processes.

• Production-native: The architecture is engineered to production service levels because business-critical applications, analytics, and agents depend on it continuously.

Production-native: The architecture is engineered to production service levels because business-critical applications, analytics, and agents depend on it continuously.

• Decentralized: It meets data where it already lives rather than requiring all enterprise data to be consolidated into a single system first.

Decentralized: It meets data where it already lives rather than requiring all enterprise data to be consolidated into a single system first.

Change data capture, event streams, stream processing, open table formats, catalogs, and low-latency serving exemplify this approach. Organizations can build a Streamhouse with open-source technologies, commercial products, or both.

Lakehouse architectures expanded the ability to store and analyze large volumes of historical data for business intelligence. A Streamhouse is designed to help applications and agents run the business as events happen. These two architectures coexist in many of the world's leading organizations, each serving their intended workloads and exchanging data through open formats.

The complete definition is available at streamhouse.com/definition.

Advancing an Open Category

The Streamhouse Working Group will work to:

• Maintain and evolve the Streamhouse definition publicly through a versioned repository.

Maintain and evolve the Streamhouse definition publicly through a versioned repository.

• Advance the technologies, open standards, and interoperability practices needed to build Streamhouse architectures.

Advance the technologies, open standards, and interoperability practices needed to build Streamhouse architectures.

• Encourage participation from customers, practitioners, technology providers, and the broader data community.

Encourage participation from customers, practitioners, technology providers, and the broader data community.

• Preserve Streamhouse as an open, vendor-neutral category that no single company controls.

Preserve Streamhouse as an open, vendor-neutral category that no single company controls.

“Applications and agents increasingly need fresh, governed data as events happen,” the five founding members of the Streamhouse Working Group said in a joint statement. “An open definition gives the industry shared language for this architecture and a common starting point for advancing the technologies and standards behind it.”

Keeping Streamhouse Open

The Streamhouse name was coined by Ververica. Under the Trademark Commitment published with this announcement, anyone may use Streamhouse to describe, discuss, implement, market, or analyze architectures consistent with the open definition without permission, notice, registration, or fees.

The complete definition will be maintained in a public GitHub repository and available through streamhouse.com/definition. The Trademark Commitment is available at streamhouse.com/trademark.

About the Streamhouse Working Group

The Streamhouse Working Group is an open industry initiative founded by Aiven, Confluent, Redpanda, StreamNative and Ververica. The group maintains a vendor-neutral definition of Streamhouse and works to advance the technologies, standards, and interoperability practices behind real-time, production-native, and decentralized data architectures.

For more information, visit streamhouse.com.