Hydrolix
Hydrolix is a streaming data lake that utilizes decoupled storage, indexed search, and stream processing to manage terabyte-scale log data efficiently. The platform reduces log data retention costs by 75% while enabling real-time query performance and eliminating the need for data aggregation or sampling.
- Data & Analytics
- Enterprise Software
- Software Only
Funding
Raised to date
$68.9MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
+1- nava.vc (opens in a new tab)investor website
- oregonventurefund.com (opens in a new tab)investor website
- pruvencap.com (opens in a new tab)investor website
- s3vc.com (opens in a new tab)investor website
- sozoventures.com (opens in a new tab)investor website
- svdcapital.com (opens in a new tab)investor website
- wing.vc (opens in a new tab)investor website
Founders
Product
Problem
Organizations struggle to efficiently manage and analyze terabyte-scale log data due to the high costs associated with traditional data retention, complex data aggregation requirements, and the need for data sampling to achieve acceptable query performance. This often leads to incomplete insights and delayed problem resolution.
Solution
Hydrolix is a streaming data lake designed to address the challenges of managing and analyzing large volumes of log data. By leveraging decoupled storage, indexed search, and stream processing, Hydrolix enables real-time query performance at terabyte scale while significantly reducing data retention costs. The platform eliminates the need for data aggregation or sampling, allowing users to work with complete datasets and gain comprehensive insights. Its architecture allows independent scaling of storage, ingest, and query resources to meet specific performance and budget targets.
Target Audience
Hydrolix targets organizations dealing with terabyte-scale log data, including those in observability, SIEM, AdTech, and AI Ops, as well as product teams and CFOs seeking to reduce data retention costs and improve data accessibility.
Features
- Decoupled storage architecture enabling independent scaling of storage, ingest, and query resources
- Indexed search capabilities for fast retrieval of relevant data without brute-force queries
- Stream processing for ingesting, enriching, and transforming log data from sources like Kafka, Kinesis, and HTTP
- High-density compression (HDX) reducing storage footprint by 20x-50x
- Support for on-premise deployment within a VPC to eliminate data transfer costs and maintain security
- Autoscaling, Kubernetes support, and cloud-native design for simplified operations at scale