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Dremio Cloud Now Available for Microsoft Azure

Dremio announced the public preview of Dremio Cloud for Microsoft Azure. This SaaS solution offers companies self-service analytics coupled with data warehouse functionality and the flexibility of a data lake, all within an environment characterized by rapid setup and deployment, automatic upgrades, scalability, and advanced data lakehouse management features. Dremio Cloud enables companies to rapidly drive value from their data by delivering faster time to value and increased flexibility, while decreasing costs.

"Dremio Cloud for Microsoft Azure is the service we have been looking for. It provides us with an easy-to-use environment for self-service analytics, helping us accelerate time to market, whilst keeping cost under control," said Tian de Klerk, IT Business Intelligence at S&P Global.

Dremio Cloud for Azure, built on Apache Arrow's columnar foundation, offers organizations rapid and scalable query performance for analytical workloads. Dremio's native columnar cloud cache (C3) provides unparalleled throughput and rapid response times on Azure Data Lake Storage (ADLS). Paired with Reflections, our cutting-edge technology to accelerate analytic queries, Dremio delivers sub-second response times for BI workloads. This eliminates the necessity for BI extracts, giving users the ability to attain quicker business insights with reduced complexity and effort.

Dremio Cloud for Azure provides seamless federated data access across organizations' environments, both in Azure and on-premises. Dremio Cloud can combine data located in cloud data lakes, leveraging modern table formats like Iceberg and Delta Lake, with existing RDBMSs. Delivering a semantic layer across all data provides companies a consistent and secure view of data and business metadata that can be understood and applied by all users. This allows organizations to effortlessly extend data access to a broader user base, resulting in quicker access and delivering greater business value. This seamless federated query access also helps eliminate data movement, reducing the dependency on complex ETL processes.

By simplifying data management and governance within lakehouse environments, Dremio Cloud for Azure increases value while decreasing costs. Dremio's next-generation lakehouse catalog provides Git for Data capabilities that make it easier than ever to build, manage, and share data products. By leveraging software development best practices for versioning, data teams can deliver an accurate and consistent view of their data lakehouse to all their data consumers and create zero-copy clones of production data in seconds for development, testing, data science, experimentation, and more. Lakehouse versioning enables workload isolation, transactional consistency, governance, collaboration, reproducibility, and easy recovery from mistakes.

"Dremio Cloud for Microsoft Azure is a game-changer for organizations seeking self-service analytics with flexibility in a SaaS environment. It provides SQL query performance coupled with unified data access and robust lakehouse management functionality. This empowers companies to efficiently unlock the value of their data, offering faster time to insights and cost-effective solutions. We're thrilled to bring Dremio Cloud to Azure, enabling our customers to experience the future of data management," said Roger Frey, vice president of alliances at Dremio.

"At Microsoft, we are committed to delivering powerful solutions that help our customers maximize the value from their data. Dremio Cloud for Microsoft Azure seamlessly integrates with our ecosystem, providing organizations with the freedom to harness their data in the way that suits them best. This collaboration marks a significant step towards achieving robust performance and affordability for analytical workloads via an open data architecture," said Aung Oo, Partner, General Manager of Azure Storage Engineering at Microsoft.

To learn more, visit Dremio.com or sign-up to try Dremio Cloud.

Published Thursday, December 07, 2023 9:14 AM by David Marshall
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