Artificial intelligence will eventually learn how to manage the data ecosystem largely on its own, which means conflicts between disparate intelligent engines will create more problems than they solve.
Backup and recovery is becoming less of an add-on to the data environment and more of a core asset.
The next version of Docker will have a built-in full distribution of Kubernetes; developers will be able to work with the familiar Swarm tools along with new Kubernetes tools under the same management dashboard.
If AI is like any of the technological revolutions of the past, it will solve a multitude of problems that currently bedevil IT operations.
The flexibility of emerging applications and the demands of users all but require a dynamic, scalable approach to infrastructure deployment and provisioning.
Projections of the demise of the cloud due to a rising IoT are in the same tradition of numerous other calls for technological obsolescence, from the mainframe to the PC to disk storage.
If there is one thing that the current crop of automation solutions excels at, it is taking over mindless operations to allow humans to concentrate on more creative aspects of fulfilling the business model.
A new generation of cloud management solutions is starting to delve into the intricacies of storage to hopefully bring the dream of true hybrid performance closer to reality.
Data integration must be built into IoT environments as a core capability, a challenge that many organizations are still struggling with in their legacy environments.
IoT infrastructure is likely to go through several permutations before it settles into the enterprise mainstream.
Managing cloud resources requires a more nuanced view of data operations than is needed for traditional IT infrastructure.
Those who want to sell new technology always try to foster a vision of what could be tomorrow, but those who buy it simply want to solve the problems of today, at least, if they are smart.
The hard numbers for the IoT that the suits are waiting for could very well materialize on the balance sheets of the upstart that has just thrown the existing business model into the trash bin.
While the vendor and provider communities have been quick to implement the abstraction and the scalability on hybrid platforms, the self-managing aspect has been a little slower to evolve.
Power efficiency is a worthy goal, but what is true elsewhere in the data center will likely be true in memory: Efficiency will almost always take a back seat to performance.
While blockchain has been shown to be useful in apps far beyond cryptocurrency, there is still a lot of uncertainty about integrating it into legacy enterprise processes and new functions surfacing in the IoT and other advanced architectures.
The two major trends affecting enterprise infrastructure are the cloud and hyperconverged systems, both of which seem to be enjoying more of a symbiotic relationship than parallel development tracks.
Is this a new beginning for OpenStack, or will the enterprise continue to face seemingly insurmountable hurdles to a seamless, distributed data ecosystem?
RPA is seen as crucial to the development of mega-scale data environments like the Internet of Things.
Before implementing an SDDC and the process of transformation, the enterprise should think about how it intends to use it and how it will reorganize itself around a highly fluid, data- and application-centric environment.
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