How to Avoid Multi-Cloud Vendor Lock-in, Part 2: Modernize Your Data Strategy

Companies have many reasons for wanting to migrate their applications and services from one cloud platform provider to another. Reasons often include the desire for application portability; regional cloud performance; changes in platform capabilities (think AI/ML on Google Cloud, AWS’s broad tool set, etc.); compliance requirements; company mergers and acquisitions; and more.

The point is, with 90 percent of companies already using multiple clouds to run apps and services, there’s a very good chance many of you will want to migrate some apps and services from one cloud to another as your requirements change over time.

Part 1 of this 2-part series on avoiding cloud vendor lock-in explored how to prepare an application strategy to operate in a multi-cloud world. This post will provide insights into the right data strategy for avoiding cloud vendor lock-in when using multiple clouds.

Four Steps to Multi-Cloud Data Freedom

Step #1: Use a Virtual Data Layer

Having full copies of data tied into a cloud provider with all requisite hooks and APIs can make switching providers a nightmare. In addition, a lot of manual effort and storage cost goes into provisioning large datasets in any cloud. Why not virtualize data whenever possible? In this way, you can use more of your data at lower cost and change providers whenever you like.

Development teams thrive on the ability to use virtual copies of any size datasets. This means more realistic testing throughout the development cycle when large datasets are involved.

 Step #2: Leverage a SaaS-based Data Management Service

Having SaaS enables companies to fulfill the promise of cloud apps, HPC in the cloud, and edge computing by providing advanced data mobility technologies, usually based on a combination of metadata pointers to existing datasets and virtual/full copies of specific datasets.

The right SaaS platform incorporates all the functionality needed to real-time manage data across cloud, hybrid cloud, and multi-cloud deployments. Such a platform establishes a single namespace for a variety of each customer’s data ─ including filesystem data, HPC data, genomics data, NoSQL data ─ regardless of where the data resides and where it is used.

Step #3: Ensure You Control Your Data at All Times

This may sound obvious, but too many organizations find themselves at the mercy of service providers, because they have given up control of their data policies, rules, procedures, security, etc. When searching out a SaaS data management service, make sure the service is delivered in a way that ensures you are able to set and change all your own data orchestration policies – without limitation. In addition, query the SaaS vendor regarding how fast data policies, rules, and specific pointers can be made and how fast they will propagate throughout the system. Speed is of the essence in many situations, and all your changes should take effect in near-real-time.

Step #4: Use a SaaS with High Performance Computing Capabilities

More and more, companies are relying on AI, machine learning, real-time analytics, and other applications that rely on fast data access. Why leverage a data management platform that cannot enable any and all performance requirements? Not to blow our own horn here, but Kmesh is the perfect example of a performant SaaS data management layer. Built on the high-performance Lustre filesystem, our SaaS platform delivers the fastest data access for all apps and services in a multi-cloud environment. In fact, Kmesh SaaS is Lustre-as-a-Service, but we manage the technology for you.

At Kmesh, we exist to help organizations operate apps and services across multi-cloud environments. Contact us today at to learn how easy it can be to free your data from cloud vendor lock-in.

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