A Multi-Cloud Strategy to Improve Openness and Choice

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6 min read

The current rush to the cloud has brought an increased focus to the advantages of cloud processing, but it’s also beginning to reveal a tried-and-true challenge that all IT professionals face: ‘vendor lock-in.’ My personal frame of reference, as a former CIO, is that it’s always something you try to avoid but are challenged in implementing. In the cloud, it’s been reframed as ‘cloud optimization.’ This tactic should be familiar to any seasoned technical person, particularly those familiar with database implementations. The reality is that an optimization focus allows the provider to deliver superior scale and/or performance by building tighter integration into the lowest levels of the platform, but at the cost of locking customers into their cloud of choice.

A former CTO I worked with, many years ago, used to say that the challenge with forcing everyone to go to standards (as we saw with Linux) is like asking all the doughnut makers in the world to use exactly the same recipe, threatening them with a violation if they add a chocolate topping. (As a huge fan of Krispy Kreme, this analogy resonated with me.) Cloud optimization represents the same thing: at the end of the day, infrastructure as a service, sold by every major cloud provider, is built on the same base architecture and platforms (filers, SSD, servers, networking, Linux), but they add their own special flavors with knowledge they have or acquired to differentiate their service from everyone else. Let’s take BigQuery from Google as an example. It is very powerful, but it’s really challenging to make that run on AWS when you may require having multiple cloud providers for business continuity, DR, or regulatory reasons. Similarly, AWS has any number of different ‘platforms’ that they provide as a service, but customers are challenged with integrating them and ensuring compatibility, as the components of the final service the customer requires are upgraded to add new features. The customer becomes the integrator.

MapR has a different philosophy when it comes to cloud providers: let them do what they do best; optimize their infrastructures to be as performant as possible; and let us take away the complexities of component integration and cross-cloud compatibility – that’s our job! We’ll leverage all the performance and price improvements from every cloud provider and ensure that customers can run on every cloud or, better yet, every combination of clouds that customers may have. That’s what a converged platform does. Our mantra is “All data, One platform, Every cloud.”

Further, in the real world, customers may have critical data that resides in a wide variety of places and may not want to move it for business, regulatory, and/or compliance reasons. MapR helps you there, too. We’re not concerned about the physical location of the data; we have a solution that covers you for both in-place analytics as well as a broader strategy of converged data for applications and analytics. In addition, we can improve the overall cost of operating in the cloud with our edge capabilities. Think of each cloud as ‘an edge device’–meaning, having data in AWS, Azure, and/or GCP–which you can then merge, performing mission-critical analytics and leveraging the data (operational or data lakes) in a single view, using MapR. These cloud deployments can be part of a much larger deployment that includes edge devices (vehicles, medical equipment, machinery, etc.) originating data that is aggregated in the cloud. You don’t have to move the data (that’s up to the customer and the use case); you only move the data that might be required, thus treating the cloud providers as ‘edge devices’ or, as I like to call them, Edge Providers. You’re moving only the data that’s required – not all the noise.

Truthfully, with on-premises, private, public, and hybrid clouds, the largest costs quickly become the backhaul or network traffic – much of which is just noise, not the critical information required by businesses and applications to take action.

To that end, we designed the MapR Data Platform to be able to run on an edge device–like an Intel NUC–as well as on a centralized system. The MapR Edge solution enables a customer to utilize analytics to determine the appropriate data to be acted upon or brought back to a central system–no matter where it resides–for more detailed analytical purposes.

As the thought leader in this space, MapR began with this original design point rather than bolting it on or adding it as an afterthought. It’s this architecture that enables us to support all data on one platform across every cloud.

Learn more about the MapR Orbit Cloud Suite

MapR Orbit Cloud Suite

With MapR Orbit Cloud Suite our vision is to empower you to unleash your full cloud potential wherever you are in your cloud journey. We do this with unique value-add capabilities across all cloud operating models including: private cloud, public cloud, multi-cloud, and edge.

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This blog post was published September 07, 2017.

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