Dremio Speeds Data Lake Queries in AWS Cloud

Dremio, a data lake specialist, today announced a new offering that speeds queries across cloud-hosted platforms such as Amazon Web Services Inc. (AWS) and Microsoft Azure.
Data lakes are crucial for Big Data analytics and other data-centric scenarios. They allow for the storage and manipulation of large amounts of data in different formats in a flat architecture, such as files or “blobs”, as opposed to relational data stores.
However, it can be difficult to quickly query such data, especially in cloud storage mechanisms like Amazon S3, which can make it difficult to access. This issue is addressed by Dremio Data Lake Engines (AWS, Azure, and Hybrid Cloud).
According to the company, its patent-pending technology speeds query execution for various data types, including JSON and text-delimited (CSV), directly from the data lakes without the need to load it into any other systems or data warehouses.
The company’s new open source platform speeds up query execution by using columnar caching, predictive pipelinelining, and a new execution engine kernel that promises to increase performance by up to 70%.
The platform supports security offerings like AWS Secrets Manager, Multiple AWS ID Roles, Server-Side encryption with AWS KMS Managed Keys, and many more.
According to Enterprise Strategy Group senior analyst Mike Leone, “Organizations recognize how important it is to be able to quickly leverage analytics services and data to support their data-driven initiatives,” he said. It’s important to have a solid data foundation. This is especially true if the data lake can be simplified to allow organizations to maximize data availability, accessibility and insights. Dremio addresses this need by providing an easy way for personnel and organizations to access the data that matters. It doesn’t matter how big or how fast it changes.
Dremio’s Data Lake Engine has a new edition.