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For organizations undergoing digital transformation, a hybrid cloud strategy is imperative for deploying and managing applications and services. Being able to deploy analytics on multiple public clouds as well as private clouds allows for greater flexibility and power.
With a hybrid cloud strategy, you’ll forever be able to keep up with the latest pricing and processing offerings from cloud and on-premises vendors and perform your analytics in whatever processing configuration is suited for any analytical workload.
For a hybrid cloud strategy to be effective, deployments need to have similar designs by offering standardized technology that enables data and application portability. In other words, it’s important to consider a flexible combination of technologies that runs your analytics and stores your data. You shouldn’t choose solutions that lock you into a single cloud.
With deployment flexibility in your list of key concerns when choosing your analytics infrastructure, you gain a hybrid cloud strategy that’s free from vendor lock-in, allowing you to decide how and where to operate your analytics projects.
There have been a few technologies that make hybrid cloud strategy a viable, affordable option.
The popularity of the S3 bucket in the clouds and its evolution to on-premises object store from many vendors is a breakthrough. S3 object storage refers to a virtual container of data that is accessed through a common API. You can store as much data as you want and as many objects as you want in an S3 bucket. Amazon AWS is the originator of the S3 bucket, but on-premises vendors like Pure, Dell, VAST, NetApp, Scality, Minio, and Cloudian support the storage technique. Other cloud vendors like Google, Microsoft Azure, and Alibaba support similar storage. You can use the same API to access data across the clouds and on-premises storage with very little noticeable difference to the users of analytics.
Databases that support the separation of compute and storage play an important role. Separating compute and storage allows database software to be more available, elastic, and scalable, and it reduces costs dramatically, which is driving the popularity of this approach. For example, Vertica can leverage either the cloud or on-premises storage bucket to offer rapid elastic scaling no matter where the data sits. The database uses the S3 bucket as its main data repository in both data warehouse and data lake deployments. A special system of caching overcomes performance problems that might be present in object storage.
Cloud-native software can be orchestrated using Kubernetes, a container orchestration system. Kubernetes hybrid cloud strategies allow teams to guide multiple Kubernetes clusters across multiple public and private clouds, with seamless application portability in mind.
There are numerous step-by-step models for developing a hybrid cloud strategy, and many technology vendors offer their own variations.
Conduct an inventory of all the analytics use cases your organization relies on today. Try to understand how relevant the workloads are, as well as how much customization they have required.
Consider today and tomorrow to best understand the objectives of your organization and your organization’s overall business strategy. What are your plans for the next three years in terms of data analytics? How much data will you have, and what will it be used for? Gaining input from senior management and business stakeholders as part of this process is essential.
If your organizations falls under local, State, Federal, or industry regulations, consider the implications of hosting applications in the cloud. Can all of your analytics be performed on public cloud infrastructure, or does it need to be held on private cloud infrastructure? Regulations such as HIPAA, PCI, and GDPR, as well as any regulations specific to your industry, will impact your strategy, since rules regarding privacy and other concerns may prohibit storage and compute operations via the cloud.
Elevating awareness of your hybrid cloud strategy to senior business leadership will help sponsor new projects and facilitate new sources of funding. Make sure you track the following benefits:
There’s a cost to have data spread out across your organization. By consolidation in object store, users won’t need to copy data as frequently. Therefore, data sprawl and all of the costs associated with it will decrease.
If the need for analytics suddenly spikes, a hybrid cloud strategy can respond efficiently. Handling the spikes means that you will be able to server your customers better and protect your SLAs, as well as your brand.
If necessary, teams can instantaneously shift analytics from one cloud to another, or to on-prem resources. There will be no complexity in reconfiguring the analytics system and no need to involve the engineering teams to rewrite code.
By duplicating on-premises and cloud infrastructure, a hybrid cloud architecture can help you deliver 24/7 availability, while also reducing mean time to recovery. You should be able to flip a switch to move your workloads, if trouble arises.
Hybrid clouds can be down-sized or completely turned off when not in use. This can help you delay much of the financial outlay as you ramp up revenue. You can move workloads between public clouds, and even take advantage of spot instances that uses spare EC2 capacity available for less than the on-demand price.
In shifting workloads from owned infrastructure to the public cloud, you effectively convert CAPEX to OPEX – and because you only pay for the resources you use, costs drop further during periods of low demand.
Explore the choices successful Vertica customers have made, and get guidance towards the solution that works for you.
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