VeeamON: From B&R to intelligent data management
Veeam's strategy for accessing the enterprise market with its hyper-availability and large-scale intelligent data management was announced yesterday at VeeamON 2018.
The company recognises that the scale and complexity of managing the hyper-growth and hyper-sprawl of enterprise data today requires a solution that moves from traditional policy-based data management to a more behaviour-based system, so data can manage itself more autonomously and deliver critical business and operational insights at speed.
"Since Veeam's founding in 2006, we have been the go-to provider of availability solutions for apps and data in multi-cloud environments," says Veeam co-CEO and president Peter McKay.
"However, as technologies like IoT, AI, machine learning and blockchain mature, and as customers grapple with mining massive amounts of data for better business insights, they need solutions that can do far more than ensure data availability. We believe hyper-availability is the new expectation for data in today's enterprise. "
Availability has been traditionally associated with business continuity and backup and recovery, to make sure organisations stay up and running.
With increasing challenges in managing enterprise data, hyper-availability requires that data must evolve from basic backup and recovery solutions, which mechanically copy data at prescribed intervals, to a much higher level of intelligence where data learns to respond instantly and appropriately to what actually happens anywhere across the enterprise data infrastructure.
Data protection and data management, McKay says, must move from reactive insurance policies to a system that provides proactive business value.
"Veeam's vision for intelligent data management is based on a flexible set of integrated, plug-and-play solutions," says Veeam vice president of product strategy Danny Allan.
"Veeam Hyper-Availability Platform provides the integration, visibility, orchestration, intelligence, and automation to evolve data management from policy-based to behaviour-based, and from manual management to intelligent automation. This enables the provisioning and management of the massive, constant flows of data running across highly distributed, multi-cloud infrastructures to be securely automated, self-learning, and optimally orchestrated.
Allan outlines the five stages the Veeam team have identified for an enterprise's transition to intelligent data management:
1. Backup - Back up all workloads and ensure they are always recoverable in the event of outages, attack, loss, or theft.
2. Aggregation - Ensure protection and availability of data across multi-cloud environments to drive digital services and ensure the aggregated view of service level compliance.
3. Visibility - Improve management of data across multi-cloud environments with clear, unified visibility and control into usage, performance issues, and operations; data management begins to evolve from reactive to proactive, preventing any loss of data availability through advanced monitoring, resource optimisation, capacity planning, and built-in intelligence.
4. Orchestration - Seamlessly move data to the best location across multi-cloud environments to ensure business continuity, compliance, security, and optimal use of resources for business operations. This requires an orchestration engine that enables enterprises to easily and non-disruptively execute, test, and document disaster recovery (DR) plans in a highly-automated fashion.
5. Automation - Data becomes self-managing by learning to back itself up, migrate to ideal locations based on business needs, secure itself during anomalous activity, and recover instantaneously. This stage brings new levels of automation to enterprise data management via a combination of data analysis, pattern recognition, and machine learning.
