What is Dremio?
Dremio is a cutting-edge, unified lakehouse platform designed to empower businesses with self-service analytics and AI capabilities. Trusted by global enterprises like Amazon and Maersk, Dremio delivers high-performance, flexible analytics on cloud, hybrid, and on-prem environments. Leveraging open-source technologies such as Apache Iceberg and Apache Arrow, Dremio reduces costs, simplifies data management, and accelerates insights—all without the need for complex ETL processes or data movement.
Key Features:
🚀 Unified Analytics
Access all your data through self-service analytics, enabling every user to derive insights quickly and efficiently.🔍 SQL Query Engine
Optimized for high-performance BI with query acceleration, delivering up to 100x faster results without data movement.🛠 Modern Data Catalog
Manage and optimize Apache Iceberg tables with automatic table management and Data as Code capabilities.💼 Lakehouse Flexibility
Enjoy seamless integration across cloud, hybrid, and on-prem environments with next-gen architecture designed for scalability.
Use Cases:
Amazon: Supply Chain Optimization
By adopting Dremio, Amazon achieved 10x faster query performance, reducing project completion times by 90%. The platform enabled quicker data insights, streamlining their supply chain operations.Maersk: Budget-Friendly Modern Data Stack
Maersk utilized Dremio to build a next-gen data platform on a budget, achieving unified analytics and integrating GenAI for advanced insights.NCR: Accelerated Project Delivery
NCR leveraged Dremio to complete analytics projects 90% faster with over 10x price performance, significantly lowering operational costs.
Conclusion:
Dremio stands out as a robust, cost-effective solution for enterprises seeking a unified lakehouse platform. It simplifies data management, accelerates query performance, and offers unparalleled flexibility across various environments. By choosing Dremio, businesses can unlock the full potential of their data, driving innovation and operational efficiency.





