Global Data Management-as-as-Service Platform, #Druva, Extends Its Capabilities

Druva has announced a major update to its Druva Cloud Platform to address the growing challenges posed as enterprises store more data in complex, heterogeneous cloud environments. The update has extended the platform capabilities for cloud applications – customers can now view and manage their data across SaaS (Software-as-a-Service), PaaS (Platform-as-a-Service) and IaaS (Infrastructure-as-a-Service).

The update would ensure Druva Cloud Platform customers that their data is properly managed through its lifecycle, meets enterprise service level agreements, and achieves consistency of service regardless of where their data is ultimately stored.

The cloud enables organizations to transition from legacy infrastructure, save money and gain business agility for their data. However, once in the cloud, organizations would experience a loss of visibility and control, making the data much harder to protect and manage. Challenges would include:

  • A patchwork of disparate systems – This can make data protection a nightmare as a mix of on-premises, hybrid and public cloud infrastructures proliferates, requiring multiple systems that must be administered separately, oftentimes in very different ways.
  • Different clouds have different data management needs – IaaS, PaaS, and SaaS have different protection and data management requirements that range from simple resiliency needs like backup and disaster recovery to more complex governance such as compliance, search and legal data handling.
  • Data protection and data management for cloud is inefficient, costly and kludgy – Using a mix of on-premises, hybrid and non-native public cloud services for data protection can significantly drive up costs and administration overhead that offset any savings from using the cloud in the first place.
  • Exponential growth of data – As the cloud provides yet another area to store and manage data, IT teams must deal with growing data lifecycle complexity, including managing data over time for long-term retention and archiving. If not done properly, lack of management can equate to high costs due to collecting too much dark data.

“Moving data to the cloud is not a panacea,” said Dave Packer, vice president of product and alliances marketing, Druva. “If a company’s data management is a mess while it exists in-house, then exporting it to the cloud can introduce even more data management challenges, and the increased cost to fix these can offset any anticipated savings.”

Druva Cloud Platform – Meeting the Callenges

Druva’s cloud-native architecture and “innovative” pay-as-you-consume Data Management-as-a-Service platform means that companies do not need to invest in additional hardware or special software. Druva Cloud Platform would readily scale to accommodate terabytes or petabytes of data due to its cloud-based architecture.

  • Protecting disparate systems – Druva Cloud Platform provides a single point of data management and protection for workloads in the cloud. With integrated visibility and management into Druva Apollo, Druva inSync and Druva Phoenix services, the platform enables enterprise customers to achieve consistency of data protection and lifecycle management across environments, including following the data when workloads move (e.g. from on-premises to VMware on AWS, to AWS native environment).
  • Different clouds have different data management needs – Druva Cloud Platform’s single management control plane would ensure that the right rules are in place for all enterprise data, as well as customizing those rules where it is appropriate or required to do so.
  • Reducing data protection and data management costs – ‘Lifting and shifting’ on-premises backup/recovery into the cloud would result in inefficient compute and storage usage, while backing up the cloud to on-premises is resource intensive, complicating administration and adding significant cost. Druva Cloud Platform is implemented natively on AWS Cloud, providing “streamlined” storage management, elasticity and scale throughout the lifecycle of data.
  • Exponential growth of data – Global data de-duplication and consolidation of multiple storage and data recovery products into one combine to reduce the growth of data storage requirements while still ensuring you can protect all necessary company data. Automated lifecycle management would reduce costs still further over time.
Furlow consulting