Multi-cloud: 5 important trends to watch

Multi-cloud: 5 important trends to watch

Some organizations went multi-cloud by accident, deploying applications on multiple cloud services without crafting a multi-cloud strategy. That’s changing in a hurry

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July 30, 2019
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4. IT teams prioritize operational consistency

Organizations that adopted multiple cloud services organically – which is perhaps a polite way of describing different teams using different services with little or no coordination or standardization – will become more consistent in how they operate these different services and environments going forward. Otherwise, teams risk the perils of running multiple clouds in disparate, non-repeatable fashion.

“As companies continue on the path toward digital transformation, it becomes more important for IT operations teams to provide ‘operational uniformity’ across diverse workloads, global footprints, and different business units at various stages of maturity,” says Bhanu Singh, SVP at OpsRamp. “IT Ops teams will need to be able to manage services consistently and evenly, regardless of the underlying legacy and multi-cloud applications, infrastructure, or platforms.”

For some, this is where DevOps culture plays a key role. Singh also sees a trend on the horizon where IT operations are delivered “as a service” across the organization from a centralized place in order to achieve uniformity across environments. “That service will need to be flexible, governed, and adaptable,” Singh says.

5. Machine learning and multi-cloud will grow together

“Big data” might no longer be quite the trendy term it was a few years back, but perhaps it’s time for a comeback: Data just keeps getting bigger.

Singh expects machine learning to play a growing role in data management and multi-cloud environments, from ingestion to analytics and more. It’s not trendy so much as necessary.

“Companies are moving deeper into multi-cloud and associated technologies like serverless and ephemeral workloads that generate data on an order of magnitude greater than anything we’ve ever seen,” Singh says. “It is impossible to operate in this environment without efficient data ingestion. This will accelerate [the] use of machine learning tools that manage data and deliver actionable insights.”

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One comment, Add yours below

Lot of the things said in

Lot of the things said in this article make sense but there are many practical problems to multi-cloud strategy.
As you mentioned, large public cloud providers penalize data moving out of their cloud but that is not the only issue. In addition, the data movement from one cloud to another is limited by bandwidth in terms of throughput and is also limited by speed of light in terms of latency. The latency becomes significant across the continents or even coast to coast.
In addition most applications are not designed to work in a multi-cloud, distributed fashion and most of them do not operate on shared data. Some customers do have multiple applications where one application runs in one cloud and second app in a different cloud that operates on data that is generated from first application but such needs are still very few.
For performance reasons, the applications always want to operate on local data. So most of the time applications running in different cloud end up having their own copy of same data. This may be acceptable in some cases but it is certainly not acceptable in all cases. It automatically puts limit on what applications you can run in multi-cloud environment.


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