Dremio Cloud on Microsoft Azure

Lightning fast query performance

Dremio Cloud on Azure, with its Apache Arrow foundation, provides lightning-fast performance at scale. It delivers the highest throughput and fastest response times on Azure Data Lake Storage (ADLS). Our innovative query acceleration technology, Reflections, enables sub-second response times on BI workloads. Additionally, native auto-scaling capabilities enable organizations to efficiently and cost-effectively handle any level of concurrent queries.

Streamlined self-service analytics

Dremio Cloud on Azure provided seamless federated data access across organizations environments, both on Azure and on-prem. It is able to combine data that’s in object storage, with existing data sources such as relational databases. By delivering a semantic layer across all that data, it  allows companies to easily expose data to different users, providing faster access to data. The seamless federated access also helps eliminate data movement,  and complex ETL processes.

Seamless Lakehouse Management

Dremio Cloud on Azure delivers robust lakehouse management capabilities, including automatic data optimization and a next-generation open source lakehouse catalog. The catalog manages data in a Git-like experience that allows data teams to treat data as a product. This enables users to experiment, test, and make changes while maintaining data quality and control. The zero data copy data mart functionality makes it quick and easy to explore, analyze, and test data without duplicating data or impacting production environments

Enable Mission-Critical BI on Azure Data Lake Storage

Dremio Cloud enables high-performing BI dashboards and interactive analytics directly on ADLS. Connect Dremio directly to your ADLS for immediate, interactive performance on your data without needing to copy it into a proprietary data warehouse.

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Enable the business to create and consume data products powered by Apache Iceberg, accelerating AI and analytics initiatives and dramatically reducing costs.