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Lakestream: The Open Stream Storage
Streams on object storage, integrated with your lakehouse. Ursa is our implementation.
Stream Storage on Object Storage
Leaderless and diskless, implemented by Ursa.
Integrated with Your Lakehouse
Streams sit next to your Iceberg and Delta tables.
Kafka and Pulsar on One Foundation
The protocol is an interface, not a data silo.
Serverless Kafka Private Preview
StreamNative Cloud now supports Serverless Kafka in Private Preview — a fully managed, elastic, pay-as-you-go Kafka experience with topic-level performance profiles.
• HOW IT WORKS
One Open Storage Layer for Every Protocol
Lakestream solves interoperability in storage, not by translating between protocols. Streams and their metadata live on object storage next to your lakehouse tables, and stateless Kafka, Pulsar, REST, and gRPC servers serve the same data.
• VISION
Streamhouse and Lakehouse, Built on the Same Open Infrastructure
Both stand on object storage, open table formats, and catalogs. The lakehouse needed one thing to be open: tables. A Streamhouse also needs the stream to be open. Lakestream is that open stream storage, so streams and tables share the same ground instead of living in separate systems.
Stream-Table Duality, Built in
Every stream is simultaneously a table — with offset monotonicity, eventual visibility, and schema consistency guarantees built in.
Zero-ETL by Design
Events are written once in open formats on object storage, so analytics and ML read what streaming apps produce without a separate stream-to-table pipeline.
Same Ground, Different Jobs
The lakehouse is data to analyze the business; a Streamhouse is data to run it. Lakestream lets both work from the same open storage.
• DEEP DIVE
Inside Lakestream
Lakestream separates stream storage, stream metadata, and protocol serving, so each scales independently while sharing the same governed data.
Stream Storage — Implemented by Ursa
Lakestream stores streams on object storage, and Ursa is our implementation: leaderless and diskless, with a distributed Write-Ahead Log (WAL) for low-latency streaming and data compacted to Parquet for analytics.
WAL for sub-millisecond write latency
Leaderless, diskless — no broker disks, no cross-AZ replication
Stream Catalog — Integrated with Your Lakehouse
Streams, schemas, and tables are governed together and registered in the catalogs your lakehouse already uses — Unity Catalog, Snowflake Horizon, AWS S3 Tables, and Apache Polaris.
Streams and tables under one namespace
Unity Catalog, Snowflake Horizon, S3 Tables, Polaris
Stateless Serving — Multi-Protocol Access
Because interoperability is solved in storage, stateless Kafka, Pulsar, REST, and gRPC servers read and write the same streams. The protocol becomes a choice of interface, not a choice of data silo.
Kafka, Pulsar, REST, gRPC — all stateless
No leader elections, no partition rebalancing
• KEY BENEFITS
Why Open Stream Storage Matters
Up to 95% Cost Reduction
Object storage eliminates cross-AZ replication. At 5 GB/s throughput, implementations show up to 95% cost savings versus traditional broker-based Kafka.
Leaderless & Diskless
No leader elections, no partition rebalancing. Stateless protocol servers scale horizontally — add or remove like web servers.
Open Formats
Data lives in Parquet, Iceberg, and Delta Lake on your object storage. No proprietary segment format, full query engine portability.
Stream-Table Duality
Every stream is simultaneously a table with built-in consistency guarantees: offset monotonicity, eventual visibility, and schema consistency.
Multi-Protocol, Single Data
Kafka, Pulsar, REST, and gRPC serve the same streams simultaneously. The protocol is a choice of interface, not a choice of data silo.
Up to 95% Cost Reduction
Object storage eliminates cross-AZ replication. At 5 GB/s throughput, implementations show up to 95% cost savings versus traditional broker-based Kafka.
Leaderless & Diskless
No leader elections, no partition rebalancing. Stateless protocol servers scale horizontally — add or remove like web servers.
Open Formats
Data lives in Parquet, Iceberg, and Delta Lake on your object storage. No proprietary segment format, full query engine portability.
Stream-Table Duality
Every stream is simultaneously a table with built-in consistency guarantees: offset monotonicity, eventual visibility, and schema consistency.
Multi-Protocol, Single Data
Kafka, Pulsar, REST, and gRPC serve the same streams simultaneously. The protocol is a choice of interface, not a choice of data silo.
• PROOF POINTS
Lakestream in Practice
Lakestream is shipping today. Ursa is our implementation of it, and Ursa for Kafka runs native Apache Kafka on it.
Ursa
Cloud-Native Storage Engine
Our implementation of Lakestream: a cloud-native storage engine with a WAL + Parquet two-tier model on object storage that makes lakehouse-native streaming possible.
VIEW DETAILS
Ursa For Kafka (UFK)
Native Apache Kafka on Lakestream
Native Apache Kafka — not compatible, native. UFK takes Apache Kafka 4.0+ and replaces local disk storage with Lakestream, delivering leaderless, diskless Kafka with up to 95% cost reduction.
VIEW DETAILSUrsa Awarded Best Industry Paper
Read our award-winning paper on Ursa, the first lakehouse-native, leaderless streaming engine for Kafka.
READ MORE
• RESOURCES
Learn More About Lakestream
What Is a Lakestream?
StreamNative Joins the Streamhouse Working Group to Advance an Open Architecture for Real-Time Applications and AI Agents
From Kafka Streams to AI-Ready Iceberg Tables: Building the Modern Lakehouse with StreamNative and Starburst
Experience Lakestream
- Spin up a Kafka or Pulsar cluster on StreamNative in minutes.
- Every topic is a lakehouse table — no connectors, no ETL.
- Up to 95% cost reduction with leaderless, diskless architecture.