Posted in Industry on Aug 01, 2018
The cloud has been incredibly effective at simplifying how applications are built and deployed by eliminating physical constraints. The vision of the software-defined datacenter has been fully realized. By turning the challenge of acquiring and provisioning hardware into a button press, the cloud has eliminated many barriers to new application deployment.
But the benefits that come with this ease of deploying and scaling systems also come with a cost. The “hidden gotcha” described by IDC’s Research Director, Phil Goodwin, refers precisely to a challenge that may not be accounted for by many enterprises.
That “data dependency” emerges directly from the ease of using the cloud to build systems that need to work on the same information.
Here’s a simple example of an application data dependency. A supply chain application will depend on product data that describes components. Changes to that master data by necessity need to occur in a product database, and will likely be queried by applications that use it for advance shipping notifications. For example, a modification to the weight of a product must be used by applications that drive receipt in distribution centers, packing and shipping calculations. Each of those applications is dependent on that product data, and will access it as a service.
But processes can become dependent on the health and utility of the platforms on which they operate. Because of this dependency, organizations cannot ignore the unique needs of systems that rely on one another.
In a sense, everything that was old is new again. The “fallacies of distributed computing” (https://en.wikipedia.org/wiki/Fallacies_of_distributed_computing) referred to eight misconceptions that still affect cloud-native systems.
Distributed systems (which includes all cloud applications) are more complex than non-distributed ones, and delegating the response to that complexity to applications alone introduce further cost and complexity. As we get better at building distributed systems at scale, we can forget these principles. Just remembering them isn’t enough, you need infrastructure that simplifies the challenges of data dependency.
The foundations for a response to these challenges, including data dependency, are already available, and can use provenly optimal approaches to the coordination of distributed processes. But while distributed systems theory is one thing, its practice is another, and the application of these approaches in itself can be complex. This means that trusted, productized implementations are required.
A LiveData platform is WANdisco’s contribution to the inherent complexities of using the cloud.
Doing this at scale is difficult, but a problem that WANdisco solves. While there are other approaches to the challenge of data dependency, the benefits of a LiveData platform are being proven at scale, with forward-thinking organizations using it in ways that simplify rather than complicate. Having ready access to all data at any scale at local speed can be a competitive advantage.
For example:
A LiveData platform combines distributed consensus with data replication, supporting selective replication of massive data sets at scale, without the need to disrupt application operation while data consistency is maintained. Changes made anywhere affect the logical copies of data held in each required location, and applications can interact with their local data without adverse impact from the latency between locations. This makes the approach uniquely well-suited for use in the cloud because it can overcome the loss of direct control over physical network and datacenter resources.
Used for cloud-native, ground-to-cloud, and wholly-isolated solutions on-premises, a LiveData platform provides a compelling foundation to take advantage of the cloud without falling prey to the hidden gotcha of data dependency.
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