Streamhouse

Learn about Streamhouse, the real-time, production-native, decentralized data architecture for applications and AI.

What is Streamhouse?

A Streamhouse is a production-native, decentralized data architecture that keeps the state of the business current and available for real-time applications and AI agents.

The five core components of a Streamhouse architecture are capture, transport, transform, govern, and serve. Streamhouse covers the entire data lifecycle from source to action.

Why now?

Streamhouse architectures have been in use for years. What's new is the name: the Streamhouse Working Group is defining the pattern for developers and the broader tech industry.

This is how naming usually goes. Once a critical mass of products and technologies enables a broadly adopted data architecture, a naming ceremony follows. The pattern of "files residing on cheap object storage, queryable for analytics like an ACID database" existed for years before the term "lakehouse" was coined.

Similarly, the architectural pattern of "durable streams that populate tables representing the current state of the business" has become widespread over the past decade. In 2015, teams might have run Apache Kafka® alongside Apache Storm to handle the capture, transport, and transform stages, then written the transformed data to a different sink for each need. Those sinks ranged from high-concurrency key-value stores for real-time apps, to data warehouses for analytics.

Fast-forward to today: Confluent Cloud, Confluent's data streaming platform, gives you the building blocks for a Streamhouse architecture. It does things that weren't really possible in the past: it is a cloud-native, as-a-service offering, and it includes features that blur the line between event streams and durable tables. That lets you run one centrally governed Streamhouse architecture in the cloud for both real-time and historical use cases.

The five components of Streamhouse

A Streamhouse architecture spans the data lifecycle, from source to action. Here are the five components, with example technologies and products:

  • Capture: Get events and changes out of operational systems as they happen. Example: Debezium change data capture (CDC) connectors.
  • Transport: Durably move and retain those events for downstream processing or consumption. Examples: Apache Kafka®, Apache Pulsar™, Amazon Kinesis.
  • Transform: Filter, aggregate, and enrich events in flight with data from AI agents or other sources. That way, downstream apps, AI agents, and end users see ready-to-use data reflecting the current state of the business. Examples: Apache Flink®, Kafka Streams.
  • Govern: Apply schemas, data contracts, quality rules, lineage, and access control once, near the source, so every consumer inherits them. Examples: Confluent Cloud Stream Governance, Aiven's Karapace.
  • Serve: Deliver the result to whoever needs it: streams for event-driven apps, Iceberg or Delta tables for analytics, low-latency queries for applications and AI agents. Examples: Tableflow to Iceberg or Delta tables, Lightning Tables, high-concurrency real-time OLAP databases like Apache Pinot™.
Diagram: sources feed a Streamhouse of capture, transport, transform, and serve, with governance across all four, delivering to AI agents, apps, a lakehouse, and a warehouse.

Use cases

Streamhouse use cases fall into eight categories:

  • Synchronize a change everywhere it is used
    • A customer updates their address. CRM, billing, and fulfillment all reflect it within seconds, with the change captured once from the source database through CDC.
    • A price change in the catalog system reaches the storefront, the mobile app, and partner feeds at the same moment.
  • Maintain one consistent state across participants
    • Inventory levels shared by the storefront, the warehouse, and the pricing engine, so nobody oversells stock another system has already allocated.
    • A ride-hailing or delivery platform where riders, drivers, and dispatch all see the same trip status.
  • Isolate service evolution and failure recovery
    • A downstream consumer is down for an hour and catches up from where it left off, while everything else keeps running.
  • Demonstrate data control without a parallel system
    • Show an auditor which fields contain PII, where they flow, and who can read them, using the same schemas, lineage, and access controls that run in production, not a separate compliance copy.
  • Monitor the business to prevent negative impact
    • A fraud detection service evaluates financial transactions as they happen, flagging them before the money moves.
    • A medical device monitoring service flags abnormal readings as they arrive.
  • Decide across the last five seconds and the last five years
    • A pricing or credit-limit decision that combines a customer's activity right now with their multi-year history.
  • Ground and continuously adapt an AI agent
    • A recommendation or operations agent whose context updates as events arrive, and which learns from the outcomes of its past actions.
  • Route a computed outcome to its point of action
    • A shipment delay is detected, and the re-route is pushed straight to the carrier's system and the customer's notification channel.
    • An ecommerce customer churn prediction triggers a retention offer in the CRM while the customer is still on the website.

Additional resources

Confluent Cloud is a fully managed Apache Kafka service available on all three major clouds. Try it for free today.

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