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Dremio Introduces AWS Edition

New elastic engines and parallel projects capabilities maximize peak performance and enable data lake insights on demand.

Note: TDWI’s editors carefully choose press releases related to the data and analytics industry. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the author's statements.

Dremio, a data lake engine specialist, released a new solution purpose-built for Amazon Web Services (AWS) that incorporates two new technologies to support on-demand data lake insights and reduce cloud infrastructure costs.

Available in the new Dremio AWS Edition, elastic engines and parallel projects technologies deliver deep automation, resource efficiency, and elastic scale enhancements. The combination of these new capabilities delivers performance gains and deep infrastructure cost savings.

Elastic engines address two critical challenges for data teams that are tightly coupled; performance and cloud infrastructure costs. Cloud software and services aren't typically architected to take advantage of the inherent elasticity of AWS and thus incur ongoing infrastructure costs for idle compute resources.

At the same time, traditional scale-out query engines are built around a single execution cluster architecture that supports multiple, dynamic query workloads. As a result, the cluster is either underprovisioned (leading to workload contention and inconsistent, degraded performance) or more commonly it is heavily overprovisioned to cover peak demand (leading to low efficiency and increased infrastructure costs).

“Data teams are struggling to process, query, and extract value from the flood of data landing in Amazon S3,” said Tomer Shiran, chief product officer, Dremio. “Direct, on-demand querying of that data remains too slow, causing data engineers to copy the data into proprietary data warehouses. Once there, performance is still too slow, as additional complex and time-consuming external acceleration technologies are required such as BI extracts, OLAP cubes, and aggregation tables.

“With Dremio AWS Edition, data teams can query the data in place in S3 with lightning-fast interactive performance while reducing their cloud infrastructure costs by over 90 percent compared to traditional SQL engines.”

Elastic engines enable data teams to configure multiple compute engines, each sized and tailored to the workload it supports and running inside customers’ own AWS accounts. Elastic engines therefore provide workload isolation that eliminates both under- and overprovisioning of compute resources, maximizing concurrency and performance while minimizing the required compute infrastructure. Elastic engines are also dynamic, spinning up automatically only when needed to service queries and elastically spinning back down when queries stop. This elasticity eliminates any infrastructure costs associated with idle compute resources.

Multitenant Dremio Environments with Deep Life Cycle Automation

Cloud software and services often require complex and manual deployment, configuration, and upgrade processes that create a fragile, error-prone environment and delay time to value. To address these challenges, Dremio AWS Edition enables multitenant instances via parallel projects with deep life cycle automation. Each instance contains all associated configuration, metadata, and data-reflection details allowing for complete isolation and enabling business units to operate fully independently while facilitating compliance.

Parallel projects also provide end-to-end life cycle automation across deployment, configuration with best practices, and upgrades, all running in customers’ own AWS accounts. This automation delivers a streamlined experience where data engineers and data analysts can deploy an optimized Dremio AWS Edition instance from scratch, start querying data in minutes, and effortlessly stay current with the latest Dremio features.

The new Dremio AWS Edition is immediately available in the AWS Marketplace. For more information, visit

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