Data Lakehouse Onehouse Secures $35M to Capitalize on GenAI Revolution
You can barely go an hour without reading about generative AI these days. While we are still in the early stages of what some have dubbed the "steam engine" of the fourth industrial revolution, there's little doubt that "GenAI" is shaping up to transform just about every industry — from finance and healthcare to law and beyond.
Cool user-facing applications might attract most of the attention, but the companies powering this revolution are currently benefiting the most. Just this month, chipmaker Nvidia briefly became the world's most valuable company, a $3.3 trillion juggernaut driven substantively by the demand for AI computing power.
But in addition to GPUs (graphics processing units), businesses also need infrastructure to manage the flow of data — for storing, processing, training, analyzing, and ultimately unlocking the full potential of AI.
One company looking to capitalize on this is Onehouse, a three-year-old startup founded by Vinoth Chandar, who created the open-source Apache Hudi project while serving as a data architect at Uber. Hudi brings the benefits of data warehouses to data lakes, creating what has become known as a "data lakehouse," enabling support for actions like indexing and performing real-time queries on large datasets, be that structured, unstructured, or semi-structured data.
For example, an e-commerce company that continuously collects customer data spanning orders, feedback, and related digital interactions will need a system to ingest all that data and ensure it's kept up-to-date, which might help it recommend products based on a user's activity. Hudi enables data to be ingested from various sources with minimal latency, with support for deleting, updating, and inserting ("upsert"), which is vital for such real-time data use cases.
Onehouse builds on this with a fully-managed data lakehouse that helps companies deploy Hudi. Or, as Chandar puts it, it "jumpstarts ingestion and data standardization into open data formats" that can be used with nearly all the major tools in the data science, AI, and machine learning ecosystems.
"Onehouse abstracts away low-level data infrastructure build-out, helping AI companies focus on their models," Chandar said.
Today, Onehouse announced it has raised $35 million in a Series B round of funding as it brings two new products to market to improve Hudi's performance and reduce cloud storage and processing costs.
Down at the (Data) Lakehouse
Chandar created Hudi as an internal project within Uber back in 2016, and since the ride-hailing company donated the project to the Apache Foundation in 2019, Hudi has been adopted by the likes of Amazon, Disney, and Walmart.
Chandar left Uber in 2019 and, after a brief stint at Confluent, founded Onehouse. The startup emerged out of stealth in 2022 with $8 million in seed funding, and followed that shortly after with a $25 million Series A round. Both rounds were co-led by Greylock Partners and Addition.
These VC firms have joined forces again for the Series B follow-up, though this time, Craft Ventures is leading the round.
"The data lakehouse is quickly becoming the standard architecture for organizations that want to centralize their data to power new services like real-time analytics, predictive ML, and GenAI," Craft Ventures partner Michael Robinson said in a statement.
For context, data warehouses and data lakes are similar in the way they serve as a central repository for pooling data. But they do so in different ways: A data warehouse is ideal for processing and querying historical, structured data, whereas data lakes have emerged as a more flexible alternative for storing vast amounts of raw data in its original format, with support for multiple types of data and high-performance querying.
This makes data lakes ideal for AI and machine learning workloads, as it's cheaper to store pre-transformed raw data, and at the same time, have support for more complex queries because the data can be stored in its original form.
However, the trade-off is a whole new set of data management complexities, which risks worsening the data quality given the vast array of data types and formats. This is partly what Hudi sets out to solve by bringing some key features of data warehouses to data lakes, such as ACID transactions to support data integrity and reliability, as well as improving metadata management for more diverse datasets.
Since it is an open-source project, any company can deploy Hudi. A quick peek at the logos on Onehouse's website reveals a diverse range of customers, from fintech startups to established enterprises.
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