Onehouse
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About
Onehouse is a cloud-native, fully-managed data lakehouse service designed to bridge the gap between the scalability of data lakes and the ease of use found in traditional data warehouses. Built by the creators of Apache Hudi, the platform provides a managed infrastructure that allows organizations to ingest, process, and store massive volumes of data in open formats like Hudi, Iceberg, and Delta Lake. By utilizing a Universal Data Storage layer, it eliminates the vendor lock-in typically associated with proprietary cloud warehouses, ensuring that data remains accessible across various cloud environments and query engines. The platform's performance is driven by the Quanton engine, which enables existing SQL and Spark jobs to run up to 2-3 times faster while reducing infrastructure costs by approximately 50%. Key components include OneFlow for near real-time data ingestion from databases and event streams, and an automated Table Optimizer that handles maintenance tasks like compaction and clustering to speed up queries by up to 30x. It also features a serverless Spark compute runtime and an adaptive workload optimizer, which helps manage resource spikes and maintain strict service-level agreements without manual intervention. Onehouse is primarily built for data engineering teams, data scientists, and organizations managing planet-scale data platforms. It is particularly effective for those looking to offload compute-intensive transformations from expensive cloud warehouses like Snowflake or Redshift to a more cost-effective lakehouse environment. The platform also caters to developers building generative AI applications by providing automated vector embedding services directly within the lakehouse, facilitating efficient data serving for Retrieval-Augmented Generation workflows. What sets Onehouse apart is its open by design philosophy and its heritage from the Apache Hudi project. Unlike many competitors that force users into a single format, Onehouse offers omnidirectional support for Hudi, Iceberg, and Delta Lake through Apache XTable, allowing users to switch between formats and engines without data migration. This interoperability ensures that data is never siloed, enabling businesses to query their data using any tool—from Amazon Athena and Google BigQuery to specialized engines like Trino or Flink—while maintaining a single source of truth.
Pros & Cons
Reduces Spark and SQL ETL costs by up to 50% without code rewrites.
Supports true interoperability between Hudi, Iceberg, and Delta Lake formats.
Automated table maintenance can accelerate query performance by up to 30x.
Eliminates vendor lock-in by storing data in the user's own cloud buckets.
Managed by the original creators of the Apache Hudi project.
Currently supports AWS and GCP, with Azure support listed as coming soon.
Requires an existing cloud infrastructure to host the physical data buckets.
Public pricing tiers for high-volume enterprise usage are not listed.
Use Cases
Data engineers can offload heavy ETL workloads from Snowflake to Onehouse to reduce data warehouse costs by 30-80%.
Data scientists can generate and store vector embeddings directly in the lakehouse for cost-efficient AI model serving.
DevOps teams can use the managed Hudi service to automate table optimizations and scale resources based on workload spikes.
Analytics teams can use Open Engines to query the same data pool using different tools like Trino or Flink without data duplication.
Platform
Task
Features
• cost analyzer for apache spark
• serverless spark compute runtime
• universal support for hudi, iceberg, and delta lake
• vector embeddings for gen ai
• multi-catalog synchronization
• automated lakehouse table optimization
• quanton sql and spark engine
• oneflow near real-time data ingestion
FAQs
Does Onehouse require me to rewrite my existing SQL or Spark jobs?
No, you can run existing jobs as-is on the Quanton engine without any code rewrites. This allows for a seamless transition while still achieving significant cost savings and performance gains.
What data formats does Onehouse support?
Onehouse provides omnidirectional support for Apache Hudi, Apache Iceberg, and Delta Lake. Users can switch between these formats and different query engines without performing manual data migrations.
How does Onehouse reduce data lake costs?
The platform uses the Quanton engine and incremental ETL processing to slash compute costs by up to 50%. Additionally, automated table optimizations minimize the amount of data scanned during queries.
Can I use Onehouse with my existing data warehouse?
Yes, Onehouse is designed to work alongside cloud warehouses like Snowflake, BigQuery, and Redshift. It can be used to offload compute-intensive prep work or share data between platforms.
What cloud platforms is Onehouse available on?
Onehouse currently runs on AWS and Google Cloud Platform. Support for Microsoft Azure is listed as coming soon to the platform.
Pricing Plans
Free Test Drive
Unknown Price• Access to Onehouse Cloud platform
• Trial of OneFlow data ingestion
• SQL and Spark job testing
• Table maintenance automation trial
• Lakehouse table optimization
• Open engine connectivity
Cost Analyzer for Apache Spark
Unknown Price• Analyze Spark ETL workloads
• Identify savings opportunities
• Free tool via pip install
• Works with existing Spark jobs
Job Opportunities
Backend Engineer - Distributed Systems (India)
Build and manage a fully open data lakehouse in minutes to slash ETL costs by 50% and accelerate query performance for data engineers and analytics teams.
Experience Requirements:
Comfortable working in Java
Experience with backend development (languages, frameworks, architectures)
Other Requirements:
Willing to relocate to Bangalore
Participate in interviews that use Java as the primary language
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