---
title: "Streamhouse"
description: "Streamhouse unifies streaming and batch in one architecture. Combine VERA engine, Apache Fluss, and Apache Paimon for end-to-end real-time analytics."
lastUpdated: 2026-10-14T07:58:16.000Z
source_url:
  html: "https://www.ververica.com/product/streamhouse"
  md: "https://www.ververica.com/product/streamhouse.md"
---

# Streamhouse. Dual pipelines are done.

**One query. Stream and table. Doesn't matter.**

The streaming lakehouse, coined by the original creators of Apache Flink®. One architecture for streams and tables, real-time and historical, ingestion and analytics. One SQL engine. One governed source of truth.

## What Is Streamhouse?

Streamhouse treats every stream as a queryable table and every table as a subscribable stream. One architecture runs ingestion, real-time processing, historical analytics, and AI inference on one governed set of data. Built on Apache Flink® for compute, Apache Fluss™ for low-latency streaming storage, and open table formats for the lake. Union Read crosses both tiers in a single query. Sub-second freshness when the business needs it. Lake economics when it does not.

## One query. Stream and table. Doesn't matter.

STREAMHOUSE STACK

| Feature | Technology |
| --- | --- |
| Compute | VERA / Apache Flink® |
| Streaming storage | Apache Fluss™ |
| Lakehouse storage | Any open table format (e.g. Iceberg) |
| Ingestion | Flink CDC |
| Query interface | Flink SQL |
| Governance | Built in |

## One house. Four layers. Zero compromise.

Streamhouse Architecture

### MATERIALIZED TABLES ON VERA

APPLICATION + COMPUTE
Declarative SQL. Freshness-driven refresh. VERA runs stream and batch on one engine, 2x faster than open-source Apache Flink®.

### APACHE FLUSS™

STREAMING STORAGE
Low-latency columnar streaming storage. Real-time KV and log, columnar 
tables, lakehouse-native architecture.

### OPEN TABLE FORMATS

LAKEHOUSE STORAGE
Your object store. Your format. Tiering and Union Read cross streaming and lake storage in one query.

### AUTOPILOT + WORKFLOW SCHEDULER

OPERATIONS
Autopilot scales parallelism in real time. The Workflow Scheduler triggers batch on a freshness threshold or cron. Deterministic capacity, no surprises.

## What holds Streamhouse up.

## Two stacks. Twice the cost. Half the answers.

Dual pipelines multiply everything that can break. Streaming for real-time. Lakehouse for analytics. Two pipelines. Two storage layers. Two query engines. Two governance models. Two on-call rotations. Data drifts. Costs compound. Engineers leave.

Lakehouses are too slow for what comes next. Iceberg and Delta tables are minutes-to-hours fresh. Acceptable for BI. Fatal for fraud detection, real-time pricing, and agentic AI. Batch was never going to power the next decade.

Pure streaming is too expensive for history. Keeping 90 days of data hot in Kafka is a budget line item nobody wants. And Kafka is not queryable. You cannot SELECT against a topic. So you build a separate lake to compensate. Now you have two systems again.

## Numbers. Not narratives.

The "WHY" You should have it.

- **WRITE PERFORMANCE**: Faster writes than a traditional batch lakehouse.
- **QUERY PERFORMANCE**: Faster analytical queries with streaming preprocessing.
- **COMPUTE THROUGHPUT**: VERA against open-source Apache Flink®
- **CHEAPER THAN KAFKA**: Apache Fluss™ stateless compute and tiered storage

## Pick a stack. Pick a compromise.

| Feature | STREAMING ONLY | CLASSIC LAKEHOUSE | STREAMHOUSE |
| --- | --- | --- | --- |
| Freshness | Sub-second | Minutes to hours | Sub-second to seconds |
| Historical queries | Expensive replay | Yes | Yes, in the same query |
| Storage cost | High, hot retention | Low | Low, automatic tiering |
| Query interface | Custom consumers | SQL, batch only | Flink SQL, stream and batch |
| Governance | Separate from lake | Lake only | One control plane |
| Lock-in | Vendor runtime | Open formats | Open all the way down |