How deeply should big data applications be service oriented?

The REST versus full-blown SOA argument comes up again, this time pertaining to big data

When it comes to big data, which is most effective -- service oriented architecture or REST interfaces?


The REST versus rest of SOA argument was settled eons ago, but that doesn't mean there still aren't areas and applications that are best served by one or the other. Some areas of big data infrastructure, for example, are better served by lightweight REST protocols, while others require a more intense SOA approach.

The REST versus SOA approach was recently tackled by Tom Nolle, writing in SearchSOA. His conclusion: REST interfaces work well for big data analytics applications, with a few exceptions. Here are a few key situations;

Big data repositories: "SOA is appropriate when something like big data repositories publish a specific set of capabilities that are bound to applications," says Nolle. Also, "SOA makes sense in scenarios where applications are expected to make use of big data more in terms of results of specific analytic or reductional processes."

Security and governance: SOA may be the best option here, Nolle says. "For security and governance, access logging and control can be explicit and highly integrated with user directories and application access controls. With REST, security and access control mechanisms likely have to be applied externally. This may be a strong argument for wrapping big data access inside a SOA component even if REST is used at the big data-product level."

User interfaces: RESTful interfaces make more sense "if applications need to know about big data as a resource set without abstraction into high-level services," Nolle says.

Both REST and SOA: If transaction work is contained within the big data component or whether big data references are spread through multiple components in the transaction.

(Photo: Joe McKendrick.)