Vertica and Dell EMC have announced the general availability of Vertica in Eon Mode for Dell EMC Elastic Cloud Storage (ECS), delivering data-driven organizations more freedom to leverage cloud innovation for analytics. According to the companies, this combined predictive analytics offering gives data-driven organizations the ability to leverage cloud innovation for analytics wherever their data resides, supporting hybrid deployments and on-premises data center use cases. By “right sizing” the compute resources for analytical queries and storage resources for data, data engineering teams can more cost-effectively and confidently manage variable workloads.
Most companies are embracing a multi-cloud and hybrid approach to infrastructure, and many workloads will remain on-premise.
“With Vertica in Eon Mode for Dell EMC ECS, organizations can choose our cloud-optimized architecture to manage variable workloads in hybrid or on-premises environments with Dell EMC’s market-leading software-defined cloud object storage platform," aid Colin Mahony, senior vice president and general manager of Vertica.
Vertica in Eon Mode for Dell EMC ECS provides organizations with operational simplicity and workload isolation to meet ever-stringent SLAs and business objectives. Dell EMC ECS provides customers with the flexibility to deploy object storage as an appliance-based solution or in a software-defined model to fit the performance and financial requirements of organizations. Together, Vertica in Eon Mode and Dell EMC ECS give companies a consistent platform for analytics across all of their environments, whether their data resides in the cloud or on-premise, or in a hybrid architecture.
"Building our partnership between Vertica and ECS enables our joint analytics customers to deliver a flexible and efficient architecture by separating compute and storage," said John Shirley, vice president of unstructured storage product management at Dell.
Vertica in Eon Mode for Dell EMC ECS enables organizations with analytically intensive needs to achieve fast analytical insight from the largest volumes of data to:
- Scale infrastructure resources independently – Storage can grow without adding expensive compute, and compute can be scaled up or down with variable or intermittent workloads.
- Isolate workloads – Business analysts and data scientists can work independently from a single source of truth without competing for resources.
- Simplify database options – Customers can experience improved node recovery, superior workload balancing, and more rapid compute provisioning.
- Hibernate compute nodes – Customers can start and stop analytics more efficiently by hibernating compute nodes when they’re not needed.