Streaming data lake platform startup Hydrolix Inc. today announced that it has raised $35 million in new funding to fuel product development, expand sales operations and implement a partner program.

Founded in 2018, Hydolix offers a streaming data lake platform designed to transform the economics of log data. A streaming data lake platform is a data management architecture that allows for the storage, processing and analysis of data in real-time as it flows into the system, without needing to store it first in a database.

Hydrolix’s platform offers a combination of stream processing, decoupled storage, high-density compression and indexed search to deliver what it says is real-time query performance at terabyte scale. It also says its compression and indexing technology dramatically reduces the cost of storing and using log data.

All storage is ‘hot’

“We’re cheaper than cold storage and faster than hot storage,” co-founder and Chief Executive Marty Kagan told SiliconANGLE, referring to commonly used storage tiers. “We’re doing index search on object storage which is an advantage over [Google LLC’s] BigQuery and Snowflake, which do full column scans and rely heavily on caching.”

Information technology organizations under pressure to cut costs often have to throw away data because of the high costs of storing it, Kana said. That limits their ability to conduct time-based analytics. Hydrolix says its technology allows organizations to access years’ worth of archival data quickly. All data is treated as hot, eliminating the need to manage storage tiers. The company claims its proprietary indexing algorithms can achieve real-time query performance at a terabyte scale.

The technology uses object storage and runs in a software container. “It’s completely stateless,” Kagan said. “All the storage is in Amazon S3 or Google Cloud Storage or Azure Blob Storage so you don’t have to worry about scaling.”

Decoupling storage from computing simplifies scalability by eliminating the need to repartition data every time a new machine is added or worry about replication errors, he said.

“When you’re stateless, none of the machines know about the storage engines,” he said. “You can bring up as many query machines as you want and bring them down again. There’s no interaction between the various components, so it’s much more resilient for scaling.”

High-scale and low-cost

Claimed to deliver savings of 75% or more on log management and analysis, the platform also retains four times more data than other solutions to reduce data and application carbon footprints. It can achieve compression rates of between 20-and 50 fold, Kagan said.

The Hydrolix platform is aimed at a small but lucrative niche category of applications that combine extreme scale and high speed. Uses include security, observability, content delivery, digital advertising, artificial intelligence and machine learning, and regulatory compliance.

The product is purely a data engine that works with stream processing platforms such as Apache Kafka and can be combined with analytical platforms like the open-source Grafana and Apache Spark, Splunk Inc.’s log analytics and Databricks Inc.’s namesake analytics platform.

The company claims strong growth, doubling revenue in the third and fourth quarters of 2023 and an additional 75% in the first quarter of this year, though it didn’t reveal absolute numbers. Notable customers included Arkose Labs Inc., MediaMath Inc., Akamai Technologies Inc. and Catchpoint Systems Inc.

S3 Ventures LLC led the Series B round, with previous investors Nava Ventures LLC, Wing Venture Management LLC, AV8 Ventures and Oregon Venture Fund LLC also participating. Including the funding, Hydrolix has raised $68 million to date.

With reporting by Paul Gillin

Image: Hydrolix