---
title: "Apache Fluss"
description: "Apache Fluss is the streaming storage layer purpose-built for real-time analytics. Unified stream and table storage for the Ververica Platform."
lastUpdated: 2026-10-14T09:40:33.000Z
source_url:
  html: "https://www.ververica.com/product/fluss"
  md: "https://www.ververica.com/product/fluss.md"
---

# The Storage Layer Streaming Deserved.

Apache Kafka® was built for event transport, not analytics. Apache Fluss™ was built for both. Proven at 3 PB scale, processing 40 GB/s with sub-second latency.

- Columnar streaming storage.
- Delta Joins that cut compute by 80%.
- Native Apache Iceberg tiering.

## How can I use Apache Fluss on Ververica?

Apache Fluss is available on Ververica as a Private Preview. Fluss, an Apache top-level project, adds columnar streaming storage so Flink jobs read only the columns they need, Delta Joins that move join state out of Flink jobs, and automatic tiering of older data to Apache Iceberg or Apache Paimon. Union Read queries hot and lake data in one SQL statement.

## Key Statistics

- **Proven Scale**: Production deployment processing 40 GB/s
- **Less Compute**: CPU & memory reduction via Delta Joins
- **Checkpoints**: From 90 seconds down to 1 second
- **Data Freshness**: Sub-second ingestion to query

## The Problem

You're using Kafka for something it wasn't built for. Your Flink jobs read 100% of every Kafka message, even when they only need a few half the columns. Stream-to-stream joins pile up 100 TB+ of fragile state in RocksDB with 90-second checkpoints.

## 4 Key Capabilities

### Column Pruning

Apache Arrow columnar format, read only needed columns. Big network savings.

### Delta Joins

Read only changed rows and needed columns. 80% less CPU and memory, 1-second checkpoints.

### Native Iceberg Tiering

Hot data on NVMe/SSD, cold data auto-tiers to Apache Iceberg on object storage

### Union Read

One SQL statement across hot and cold data transparently

## Production Proof

You can't ignore

- 80% Less CPU & Memory
- 100 TB+ State Removed
- 30% Cost Reduction
- 1s Checkpoint Time

## Technical Specification

| Feature | Specification |
| --- | --- |
| Storage Format | Apache Arrow columnar + row-based log (dual format) |
| Query Interface | Flink SQL (streaming & batch), StarRocks, Spark |
| Data Freshness | Sub-second (single-digit millisecond typical) |
| Throughput | 40 GB/s proven in production (3 PB deployment) |
| State Management | Delta Joins: externalized from compute engine |
| Hot Storage | NVMe/SSD with primary key indexing (RocksDB) |
| Tiered Storage | Automatic tiering to Iceberg/Paimon on object storage |
| Column Pruning | Read only needed columns (Arrow columnar format) |
| Checkpoint Impact | 1-second checkpoints (down from 90s in comparable systems) |
| Compatibility | 100% Flink Table API / SQL compatible |