Integration Technologies - Guide to Cloud Computing for Business and Technology Managers - page 132

transformations within the broker that are dependent on that end point. No
other business applications have to be made aware of the change or be modi-
fied. Thus, the number of interfaces from applications to the broker is reduced
to N plus the number of interfaces required to transform from the broker to
these very applications would be N, resulting in a total of 2 × N interfaces.
Thus, by introduction of the broker, the problem complexity reduces broadly
from N × N to N + N—a dramatic reduction in the reduction in complexity
(see Chapter 4, Section 4.2.3.3, “Hub-Spoke Integration”).
There is a downside here in that the brokers are potentially a
performance bottleneck, as all the messages between applica-
tions must pass through the broker. Good brokers support repli-
cation and clustered deployments to scale their performance.
But of course, this increases deployment and management complexity
and, more than likely, the license costs associated with a solution.
5.12.2 Enterprise Systems with an Enterprise Model
So message brokers are very useful, but not a panacea by any means for inte-
gration architectures. There is however a design approach that can be utilized
that possesses the scalability of a point-to-point architecture with the modi-
fiability characteristics of broker-based solution. The solution is to define an
enterprise data model (also known as a canonical data model) that becomes
the target format for all message transformations between applications. For
instance, a common issue is that all your business systems have different data
formats to define customer information. When one application integrates with
another, it (or a message broker) must transform its customer message format
to the target message format (say) the canonical customer information format.
Using this canonical message format, a message exchange is now reduced
to the following steps:
1. Source application transforms local customer data into canonical
customer information format.
2. Source sends message to target with canonical customer informa-
tion format as payload.
3. Target receives message and transforms the canonical customer
information format into its own local customer data representation.
This implies that each end point, that is, business application must know
• How to transform all messages it receives from the canonical format
to its local format
• How to transform all messages it sends from its local format to the
canonical format