Why In-Cloud Data Protection Architecture Matters By Amit Mehta, Director- Modern Data Centre, Dell Technologies

Why In-Cloud Data Protection Architecture Matters

Amit Mehta, Director- Modern Data Centre, Dell Technologies | Wednesday, 18 September 2019, 06:01 IST

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Amit Mehta, Director- Modern Data Centre, Dell TechnologiesThe meaning of data protection has not changed much over the last two years. Data protection simply means backup, restore, and archive. Up until a few years ago, enterprises had no idea of the potential and challenges of data. Data is your most valuable asset; many organizations are looking to protect data and address compliance. Traditional encryption software solutions are difficult to deploy and manage, lack the capability to scale across platforms and can have negative performance impacts for users.

“As more organizations move production applications to the cloud, they often find that they still need to run the same data protection workflows to support the same business operations that they did when their applications were on-premises”

The percentage of cloud infrastructure service users running production applications in cloud platforms is growing at a good pace. It is fascinating to note that the most common cloud infrastructure use case just a couple of years ago was for applications running on on-premises infrastructure. Backup is still an established use case today. However, running production applications as a cloud infrastructure service is now, the most-cited use case for cloud in frastructure services, followed closely by running business intelligent queries and running test and development environments. While organizations continue to embrace digital transformation for both their business and IT groups, the use of public cloud infrastructure has become a key driver for that transformation. Organizations are using cloud infrastructure services to moderate the capital and operational expenses associated with traditional IT hardware deployments. Clearly, the cloud infrastructure services paradigm is evolving.

ESG recently completed its annual IT spending intentions survey of 600 senior IT decision makers at midmarket (i.e., 100 to 999 employees) and enterprise (i.e., 1,000 or more employees) organizations. As part of that research, respondents were asked about their organizations’ usage of public cloud services. The research found that 49% of respondents indicated they were currently running production applications on cloud infrastructure.While previous ESG research has demonstrated a consistent synergy between public cloud infrastructure and data protection functions, the use of these services to support production applications has been steadily increasing over the years. In fact, ESG research shows a steady year over year increase from 2015, when 27% of respondents indicated they were running production applications on IaaS, to 49% in 2019. As the amount of in-cloud, business-critical production applications increase, so too does the need for cost-efficient enterpriseclass and in-cloud data protection. And it’s important to remember that even though a cloud provider may have SLAs for the infrastructure that applications run on, protecting the data in those applications at the granular level, what business requires is the user’s responsibility

                                               Why InCloud Data Protection Architecture Matters

As more organizations move production applications to the cloud, they often find that they still need to run the same data protection workflows to support the same business operations that they did when their applications were on-premises. These workflows commonly include application-aware backups with application-consistent restores that support other requirements such as disaster recovery, business continuance, test, and development. Organizations may not be able to achieve these types of recoveries or meet the SLAs of the business using the native data protection services provided by cloud providers. These services typically rely on volume-based snapshot technologies, which often cannot meet the consistency and granularity of data recovery required by key business applications.

According to our Global Data Protection Index, nearly all organizations using public cloud are also leveraging it as part of their data protection infrastructure. When considering data protection solutions in a public cloud environment, the growing data universe plays an especially critical role. Scalability option is key here.

The top use cases for data protection within public cloud include:

• Backup/snapshot services to protect workloads developed in public cloud using new application architectures

• Backup of on-premises workloads/data

• Protecting specific SaaS apps

• Cloud-enabled versions of on-premises data protection software to protect public cloud workloads

A major component of any in-cloud, hybrid, or onpremises data protection solution is the backup repository. This is where the backup images of protected data are stored. If not managed efficiently, the backup repository can grow large very quickly as more backup jobs complete over time, systems are added to the protection schema, and the amount of production data grows naturally over time. Historically, backup architects leveraged a combination of tape volumes, simple disk pools, and disk storage with deduplication technology for backup repositories. More recently, object storage has been added to the mix as IT professionals start to leverage more cloud-based features in their data protection solutions.

So, why does in-cloud data protection architecture matter? Because organizations are moving more business-critical applications to the cloud and they still need to provide enterprise-class data protection for these applications at a reasonable cost. Inefficient use of any one of the cloud resources can have a major impact on the cost of data protection. With small cloud deployments, it’s hard to find a cost advantage between the audited solutions. However, just as with on-premises data protection, choosing the wrong solution for the initial deployment can have a major impact on cost efficiency as the solution scales.

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