Today organizations spend far too much time managing data across data lakes, data warehouses and other siloed data sources. Together, VAST Data and Vertica deliver a unified data analytics platform that helps enterprises consolidate their structured, semi-structured, and unstructured data silos on a flash-powered data lake.
Data-driven customers need more speed than ever, especially as users and datasets grow. Vertica expands on this mission by providing organizations with a flexible and efficient way to scale compute performance independent of storage, while delivering workload isolation for different types of analytics. In line with that mission, storage must also be architected to ensure real-time performance even as users, applications, and datasets scale.
ENTER VAST DATA. With its unique disaggregated shared everything (DASE) architecture, VAST Data’s Universal Storage allows customers to benefit from real-time storage performance for all their use-cases, ranging from data-warehouse analytics and ad-hoc queries to complex data science jobs. This combined with exabyte levels of scalability and multi-tenant quality of service tools, makes it possible for customers to consolidate all their data onto one scale-out tier of affordable flash.
Massively-parallel architecture delivers concurrency and real-time access to all your data
Vertica queries run queries 3x faster when powered by Universal Storage
Get dedicated Quality of Service (QoS) for Vertica sub-clusters supporting different departments
Elastically scale storage as the database grows from terabytes to multiple petabytes and beyond
2x better data center density and all-flash performance at 1/2 the cost
Running Vertica Sub-Clusters on VAST Data
Massively parallel processing enables unlimited concurrency and scale
Superior data-reduction and storage efficiency lowers TCO
Allocate dedicated compute pools for Vertica sub-cluster QoS
A New Type of Storage Architecture for Vertica
Most storage technologies just repackage the 20-year old shared-nothing, scale-out architecture, which struggle to reliably scale performance and capacity beyond a few petabytes. With it’s DASE (Disaggregated and Shared Everything) architecture, VAST Data has reimagined every aspect of what has become typical in storage system design, delivering superior scalability, resilience, and Quality of Service (QoS) at a radically lower TCO for your rapidly evolving data analytics applications.
DASE allows users to scale the performance, independently from the capacity of their system, allowing you to scale to 100s of petabytes and TB/s within the same namespace.
Get real-time responsiveness for all your data. With no east-west cluster traffic, DASE enables virtually unlimited linear, predictable scale. VAST systems in production regularly exceed 1000 GB/s.
Enable access to the same data via S3 and NFS simultaneously, eliminating the need to create multiple copies of the same data. Simply write via S3 and read that same data back via NFS or vice-versa.
Pool compute servers to provide dedicated QoS for competing applications on the same namespace. Now you can run batch and interactive analytics on the same namespace.
VAST’s unique similarity based data-reduction provides the industry’s highest level of data-reduction, allowing you to get better storage efficiency.
Add enclosures and compute servers of multiple generations into a single cluster and namespace, eliminating the need for forklift upgrades or migration of data from the old to new clusters.
Enable data scientists to choose from a comprehensive list of analytical capabilities that include SQL, Python, AI and ML, time-series, geospatial and more. Connect it to your ecosystem of ETL and BI tools for easy integration.
Consolidate data warehouses and other siloed data on VAST Data as a single, unified repository for Vertica and deliver fast, scalable data warehouse analytics on structured data.
Vertica leverages ORC, Parquet, JSON, Avro and text formats in your VAST data lake and provides analytics for semi-structured data. Seamlessly mix and match data in the data warehouse and data lake.
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