The startup operates a data analytics platform that enables rapid analysis of large datasets, handling tens of terabytes to exabytes with trillions of rows. By ingesting billions of rows per second and providing filtered aggregate results, the platform simplifies complex data ecosystems for organizations.
Funding
$147.7M raised to dateRaised 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.
Founders
Product
Problem
Organizations struggle to efficiently analyze massive datasets, often facing limitations in speed, scalability, and cost-effectiveness when using traditional data warehouses. Existing solutions can be slow, expensive, and unable to handle the complexity of modern data types and analytics workloads. This can lead to delayed insights, missed opportunities, and increased operational costs.
Solution
Ocient provides a hyperscale data warehouse designed for real-time analysis of complex datasets. The Ocient Hyperscale Data Warehouse enables users to transform, stream, and load data directly, returning previously infeasible queries in seconds. By bringing storage adjacent to compute, Ocient maximizes performance on industry-standard hardware and offers flexible deployment options, including on-premises, OcientCloud, and public cloud environments. The platform supports a range of features, including native support for semi-structured data, geospatial data analysis, and machine learning, all accessible through standard SQL interfaces.
Target Audience
Ocient targets organizations across industries, including AdTech, telecommunications, government, financial services, and operational IT, that require high-performance analytics on large, complex datasets.
Features
- Hyperscale ETL service that transforms data during stream or file loading
- Support for structured and semi-structured data, including arrays, tuples, and matrices
- Geospatial data analysis capabilities with functions for ST_POINT, ST_LINESTRING, and ST_POLYGON
- In-database machine learning with OcientML, enabling model building, training, and deployment using SQL
- Compute Adjacent Storage Architecture with NVMe drives located adjacent to the processor
- Support for standard query interfaces, including SQL, JDBC, ODBC, and Python API
- Workload management features, including scheduling priorities, resource limits, and service-class settings
- Flexible deployment options: OcientCloud, public cloud, and on-premises