Dremio Goes AWS With Support for on-Demand Data Lake Insights

Data lake engine company Dremio has announced a new offering purpose-built for Amazon Web Services (AWS) to support on-demand data lake insights and to reduce cloud infrastructure costs from idle data-centric systems.

Dremio offers various tools that help to streamline and curate data, and earlier this year raised US$70 million. It also offers a data virtualization toolkit to bridge the gap between data sources such as relational and NoSQL databases and Hadoop clusters.

Elastic engines

In this case, the company’s elastic engines enable data teams to configure any number of compute engines, each sized and tailored to the workload it supports and running inside customers’ own AWS accounts. This provides workload isolation and eliminates under- or over-provisioning of compute resources, spinning up automatically when required, or powering down when unneeded.

This dramatically reduces the complexity of using the cloud for data analysis while simultaneously reducing the cost of doing so – allowing organizations to harness the full potential of their data lakes.

According to Adrian Daniel, the head of data platforms at a financial services company, Dremio AWS Edition makes it more cost-efficient to run business intelligence tools such as Tableau on AWS S3 data lake storage and accelerates queries for his firm’s predictive analytics models.

“The low-latency SQL interface, highly elastic compute engines, and self-service semantic layer will dramatically lower our cloud infrastructure costs while empowering our data analysts to explore data and derive new virtual datasets with minimal dependency on engineering,” said Daniel.

“Data teams are struggling to process, query, and extract value from the flood of data landing in Amazon S3. Direct, on-demand querying of that data remains too slow – causing data engineers to copy the data into proprietary data warehouses. And 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,” explained Tomer Shiran, the chief product officer at Dremio.

“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.”

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