Improved cloud-native analytics

VAST Data has formed a strategic partnership with Dremio to enable enterprises to get from data to insights faster with a hybrid, multi-cloud architecture for scalable analytics. Regardless of physical location – on-premises or in the public cloud – Dremio customers can now analyze their data anywhere by leveraging VAST’s massively parallel architecture for concurrent and near real-time data access at any scale.

  • Friday, 30th September 2022 Posted 2 years ago in by Phil Alsop

VAST and Dremio are at the forefront of a market shift away from siloed data warehouses and legacy data platforms such as Hadoop. As businesses struggle with the exponential growth of data volumes and data sources, they need a highly-scalable solution for storing that data, and providing broad and concurrent access for a wide range of technical and non-technical data consumers. Paired with Dremio, VAST's Universal Storage enables organizations to escape the restrictive, walled garden environment of Hadoop and the Hadoop File System. It provides customers with an open data lakehouse platform that powers the data management, data governance, and enterprise analytics capabilities typically found in a data warehouse, powered by an all-flash data store that is purpose-built to manage large volumes of structured, semi-structured, and unstructured data. 

 

In the spirit of public cloud object storage offerings like Amazon Simple Storage Service (S3), VAST unifies an organization’s data for analytics on a common, single-tiered and linearly scalable data platform - while also enabling customers to step into an all-flash S3 experience without the flash expense that’s common with conventional systems. Dremio provides an open data lakehouse platform that executes lightning-fast SQL queries using a common semantic layer across data sources, and a simple user interface. As a result, organizations can build capabilities that are superior to even public cloud offerings with cloud-native infrastructure that provide choice and flexibility on how and where data is managed.

 

“Partnering with VAST ensures Dremio users are equipped with the lakehouse data capacity and scalable high performance necessary to run their business intelligence workloads and data analytics applications,” said Roger Frey, Vice President of Alliances at Dremio. “As data volumes continue to grow, VAST’s disaggregated architecture enables users to easily scale the performance and capacity that businesses demand, and that our open data lakehouse platform delivers.” 

 

Faster time to data access

Dremio’s open lakehouse platform enables organizations to query data directly in the data lake – and on S3 architecture – without having to copy or move data. By querying data in place, Dremio eliminates the need for complex and brittle ETL pipelines and data copies. Dremio reduces the time required to fulfill data access requests from weeks or months to just hours, and makes data teams more productive. Dremio also centralizes security and governance, and its no-copy architecture reduces network and storage costs.

 

Dremio’s simplified data architecture complements VAST’s all-flash Universal Storage storage platform, which reduces latency and delivers a high-performance infrastructure for analytics at any scale. VAST’s breakthrough Universal Storage data platform reduces the amount of storage capacity necessary in cloud-native environments without compromising performance, optimizing spend and space. Together, Dremio and VAST accelerate access to data for analytics, and deliver insights to a wide range of data consumers.

 

“We continue to see high market demand to underpin organizations’ modern data analytics infrastructure with VAST. Partnering with ecosystem leaders like Dremio drives a new approach to data analytics,” said Jeff Denworth, chief marketing officer and co-founder of VAST. “Partnering with Dremio ensures that our mutual customers have an optimized and simple out-of-the-box experience as they embrace a cloud-native architecture for their rapidly evolving data management needs.”