Dremio provides a unified lakehouse platform that combines the flexibility of data lakes with the performance of data warehouses, utilizing Apache Iceberg for efficient data management and optimization. This solution enables organizations to perform high-speed, self-service analytics on all their data without the complexities of traditional ETL processes, significantly reducing total cost of ownership and time to insight.
Funding
$407M 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.
ASFounders
Product
Problem
Traditional data warehouses often require complex and costly ETL processes to move and transform data before analysis. This creates delays, increases total cost of ownership (TCO), and limits the ability to perform real-time analytics on data lake data.
Solution
Dremio provides a unified lakehouse platform that enables high-performance, self-service analytics directly on data lakes. By leveraging Apache Iceberg, Dremio eliminates the need for complex ETL processes, allowing organizations to analyze data where it resides. The platform's intelligent query engine delivers sub-second performance across all data sources, while its integrated data catalog and automated data optimization simplify lakehouse management. Dremio's architecture supports both cloud and on-premises deployments, offering flexibility and scalability for diverse infrastructure needs.
Target Audience
Dremio targets data-driven organizations across various industries seeking to modernize their analytics infrastructure, reduce costs, and enable self-service access to data for analysts and data scientists.
Features
- Unified analytics engine for querying data lakes and other data sources without data movement
- Apache Iceberg integration for efficient data management, versioning, and dataops
- Intelligent query engine with transparent query optimization and acceleration
- Integrated data catalog for discovering, understanding, and governing data assets
- Automated data optimization to improve query performance and reduce storage costs
- Support for SQL queries and standard BI tools
- Data as Code functionality
- Universal semantic layer