Cloud Strategy

Is Big Data Suitable for Multi-Cloud World?

Multi-cloud is the default state of many businesses today. According to the Flexera 2021 State of the Cloud Report, 92% of enterprises have a multi-cloud strategy, and 82% have a hybrid cloud strategy. However, this rapid shift to hybrid and multi-cloud data environments is causing roadblocks for big data management. In this article at datanami, Alex Woodie explains some big data management challenges in a hybrid and multi-cloud world.

Challenges in Hybrid and Multi-Cloud World

Moving to the cloud doesn’t solve any data management challenges. Instead, it exponentially amplifies them. The exploding data volumes create more silos, making it more challenging for your enterprise to connect, transform, and manage. Here are some of the common challenges that most organizations working with multiple cloud service providers face.


In a multi-cloud environment, enterprises find it tough to monitor and secure different systems as there is no single control point. As developers create foundational architectural services for data backup, they must also develop capabilities for cloning data, masking certain elements, and storing replicated data.


As data becomes fragmented, it risks environments by creating data silos that are harder to manage and sustain.


As organizations start deploying multiple clouds, they lack visibility across divisions.


Highly scaled and automated IT cloud platforms can hide the data’s geographic location—both from the customer and the service provider. This can lead to regulatory violations.

Mistakes to Avoid

Manual Approaches

Practices such as hand-coding are expensive to develop and challenging to manage in a hybrid or multi-cloud world. This method has several drawbacks that makes it unsuitable for modern data cloud management. Hand-coding requires skilled developers and lacks reusability. Additionally, it is a time-consuming process that undoubtedly hampers innovation and agility.

Multiple Point Products

Bringing together the disjointed products means organizations are consigned to changing roadmaps, inconsistent data governance and quality, and project overruns.

“The best way to deal with this big data complexity is to create a framework that looks at all of the data assets holistically and allows data governance policies to be applied across the lakes,” explains Woodie.

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