Data warehouses have proven to be great repositories for enormous amounts of critical information, but the process of preloading data into the necessary structure to run business analytics workloads can take weeks. One startup company has found a way to change that model by delivering sub-second query response times using cloud data lakes instead.

Dremio Inc. is a next-generation data lake engine designed to deliver self-service access and super-fast queries directly on Amazon Web Services Inc., Microsoft Azure or private cloud data lake storage. By using in-memory caching architected into the S3 format, Dremio can dramatically accelerate data access and bypass the time-consuming extract/transform/load or ETL process common to data warehouses.

In anticipation of the AWS Startup Showcase: Innovations With Cloud Data — set to kick off on March 24 — SiliconANGLE’s livestreaming studio, theCUBE, spoke with Isha Sharma (pictured), director of product management at Dremio, who appeared with theCUBE’s John Furrier in an exclusive interview. (* Disclosure below.)

“Dremio is the data lake service that essentially allows you to very simply run SQL queries directly on your data lake storage, without having to make copies,” Sharma explained. “Dremio is bringing you that fast time to value with a no-copy data strategy while providing you with flexibility to keep your data in data lake storage as the single source of truth.”

Democratizing data analytics

The company’s no-copy data strategy is grounded in a mission to democratize data analytics. By opening up the ability for users to take more advantage of data lakes, the self-service opportunity takes on greater meaning.

“Data democratization, as much of a great concept as it is in theory, comes with its own challenges in terms of all of those copies that end up being created to provide the ‘self-service experience,’” Sharma said. “With all of these copies comes the cost to store all of them. You’ve just added a tremendous amount of complexity and delayed your time to value significantly.”

One of the tools that has enabled Dremio’s data lake model is Apache Iceberg, an open table format for huge analytic datasets. The other key solution is Delta Lake, an open-source storage layer that makes data lakes more reliable.

“Thanks to technologies like Apache Iceberg and Delta Lake, there’s this ability to give your data a table structure,” Sharma said. “You have the ability to do transactions, record level mutation, versioning, things that were completely missing from a data lake architecture before. That starts to bring the capabilities that a data warehouse was providing to the data lake.”

Dremio recently closed a $135 million series D funding round, giving it a post-money valuation of $1 billion. By making full datasets available in cloud native storage and eliminating the need to move or copy data to a warehouse for analytics processing, Dremio is providing flexibility and control for data architects and self-service for data consumers.

“It used to be data lake or data warehouse, and you pick one. You probably have both, but you’re not bridging either to their highest potential,” Sharma said. “Now you’ve got this coming together of both. It’s been fantastic to see.”

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s CUBE Conversations. (* Disclosure: Dremio Corp. sponsored this segment of theCUBE. Neither Dremio nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE