Onehouse Automates State-of the-Art Apache Hudi Data Lakehouses in Four Easy Steps and Unlocks Native Performance Accelerations Inside Databricks and Snowflake With Onetable

Onehouse, the first managed data lakehouse company, today announced a new feature Onetable that advances its vision for easier, cheaper and faster data lakes. Onetable lets users take advantage of all the scale, interoperability and cost benefits of data lakehouses built on the popular Apache Hudi open source project, while fully leveraging native performance accelerations in Databricks and Snowflake.

Hudi was created by Onehouse founder Vinoth Chandar at Uber in 2016, was downloaded more than 10 million times last year and is widely used by the largest enterprises in the world.

Onehouse automates the foundation of the data lakehouse in four easy steps, saving months of extensive engineering efforts. Users securely connect their cloud account and then Onehouse quickly ingests, auto-optimizes and manages powerful incremental ETL pipelines to deliver minute-level analytics on the data lake. Data in Onehouse can then be readily accessed from best-of-breed data warehouse engines (AWS Redshift, Google BigQuery, Snowflake), data lake engines (AWS EMR, Databricks) and interactive analytics engines (Presto, Trino, AWS Athena).

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“Onehouse’s unique Lakehouse technology has enabled us to strengthen and streamline Stellarworx, our fast-growing talent marketplace for STARs – the 70+ million U.S. workers skilled through alternative routes – accelerating our collective efforts to rewire the labor market. This has saved us years of time and countless dollars and allows Opportunity@Work to leverage the power of sophisticated data analytics to boost career mobility for skilled workers across the country,” said Opportunity@Work Chief Technology Officer Kelcey Reed.

Today Onehouse is releasing Onetable so Hudi data lakehouses can fully leverage native performance accelerations in Databricks and Snowflake, by interoperating with their respective open metadata layers Delta Lake and Apache Iceberg. Onetable avoids data fragmentation by removing the need to copy data around and offers a wider ecosystem selection of highly performant engines. Now with Onetable, organizations can save costs by offloading data management from proprietary high cost services offered by cloud warehouses, while also streamlining their data architecture with much needed interoperability to support today’s diverse data use cases like traditional analytics, stream processing, data science, machine learning and artificial intelligence.

Onehouse founder and CEO Vinoth Chandar said: “Over the past year, we have built a first-of-its-kind cloud product to get data lakes up and running with just a few clicks. With Onetable, we are addressing a huge gap in the market around data interoperability, while enabling our customers to use Onehouse seamlessly with any major query engine.”

$25 Million in Series A Funding

Today Onehouse also announced that it has raised $25 million in Series A funding, bringing the total raised to $33 million. This round was co-led by Addition and Greylock, who both co-led the seed round. Onehouse plans to use the money to continue to advance Onehouse and grow the team to meet the market demand.

Addition Investor Aaron Schildkrout said, “I strongly believe that the next-gen data stack delivers dramatic cost savings by moving a ton of workloads onto Lakehouses. Onehouse’s managed cloud product is best-in-breed — a genuinely open system offering built-in data ingestion, streaming speeds, the ability to update stored data, and strong integrations with machine learning and AI applications. The new onetable feature allows organizations to get these tremendous benefits without giving up the power of industry-leading query and compute engines like those offered by Databricks and Snowflake. Best of both worlds and where every savvy data-first company is heading”

Jerry Chen from Greylock Partners said, “Onehouse continues to play a major role in shaping the emerging data lakehouse market by delivering core infrastructure needs like data management, ingestion, performance tuning, and interoperability with the ease of a cloud data warehouse.”

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