Earthmover provides a cloud-native platform specifically engineered for managing and processing large-scale tensor and climate data. This architecture accelerates data-intensive applications by eliminating I/O bottlenecks for AI/ML and data science workloads. The platform offers scalable APIs and git-style version control to streamline collaboration and production-ready data delivery.
Funding
$7.8M 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
Scientific data teams often struggle with fragmented data management due to the use of diverse file formats and storage solutions for multidimensional array data. This makes it difficult to fully leverage the data's value and hinders collaboration. Existing data storage providers are not optimized for multidimensional arrays.
Solution
Arraylake is a cloud data lake platform designed specifically for multidimensional scientific data, providing a centralized solution for storing, organizing, and analyzing large datasets. It offers a unified catalog and a high-performance cloud-native API, enabling teams to access and query data efficiently. Arraylake supports ACID transactions and rich metadata utilization, enhancing collaboration and reproducibility in scientific research. The platform allows users to leverage the scalability of object storage with the flexibility and queryability of a database.
Target Audience
Arraylake's primary customers are scientific data teams in fields like weather and climate research who need a centralized platform for managing and analyzing multidimensional array data.
Features
- Unified catalog for storing and organizing multidimensional scientific data
- High-performance cloud-native API for data access and querying
- Support for ACID transactions to ensure data integrity
- Rich metadata utilization for enhanced browse and search experiences
- Integration with open-source libraries and data formats commonly used in scientific research
- Immutable data references for reproducible and verifiable data-driven workflows
- Permission structure for secure and efficient collaboration
- Integrations with cloud object storage providers