Vertica to deliver cloud analytics to on-premises environments

Vertica today announced a new integration with NetApp StorageGRID to deliver the advantages of cloud analytics to on-premises environments. This combined analytics offering enables data-driven organizations to elastically scale capacity and performance.

This is led by data volumes’ growth and as analytics and machine learning become a strategic business drive in enterprise data centers.

Vertica with NetApp StorageGRID allows organizations the freedom to adopt cloud innovation for analytics wherever their data resides, without assuming the risks, costs, and complexities of cloud migration.

Also, providing customers with the flexibility to deploy object storage as an appliance-based solution or in a software-defined model. It gives companies a consistent platform for high-performance analytics and machine learning at enterprise scale.

“With this NetApp integration, we are committed to providing our joint customers with the broadest options to power their strategic analytical and machine learning initiatives in the way that works best for their businesses,” said Colin Mahony, general manager, Vertica, said.

The combined offering delivers high-performance analytics and machine learning with enterprise-grade object storage to enable organizations to,

Elastically scale the analytics platform’s capacity and performance as the database grows from terabytes to petabytes with NetApp StorageGRID scale-out architecture.

Isolate workloads based on specific department needs and use cases without competing for resources with the Vertica Unified Analytics Platform. The separation of compute and storage architecture of Vertica in Eon Mode allows administrators to use NetApp StorageGRID as the main data warehouse repository or as a data lake.

Fine-tune data availability, performance, locality, retention, protection, and storage costs with NetApp StorageGRID metadata-driven policies and adjust these parameters dynamically as the business value of your data evolves.