This article is the second in a series dedicated to the ESB integration architecture. The initial article is located here.
Traditional data integration
In my previous post, I talked how an ESB architecture is – to my opinion – the go to architecture to tackle an Enterprise challenges in today’s world.
But in order to understand why I value an ESB, I find essential to explain first the shortcomings of a traditional/point-to-point data integration as well as some of the push backs I initially experienced.
In a traditional data integration, interfaces are built between applications and systems through direct connections as a Point-to-Point integration. As such, the number of interfaces tends to grow exponentially as the data needs increase, and/or the landscape of application and systems expands.
Some refer to this architecture as a “spaghetti architecture”, because all systems have to be interconnected with each other in order to speak to each other.
Traditional interfaces are punctually easy and cheap to build. By punctually I mean: each interface taken separately without looking at the system in its globality, in terms of flexibility and scalability.
Below are some of the arguments I faced when talking about the need to introduce an integration layer:
- There are only 2 teams involved. No need for a third “integration team” that needs to be brought up to speed on each system’s requirements.
- There are only 2 systems involved in each interface, limiting the points of failure and troubleshooting efforts.
- Sender and receiver teams can quickly discuss the data requirements and come up with a format satisfying the needs of the interface as well as a transport method satisfying each system’s technical limitations.
- With this type of interfacing, the sender and/or the receiver remain in control of the data and transformations hence they can troubleshoot issues faster without having to rely, once again, on a “third team”.
The points above are very valid. When you operate in a world where there are few senders and receivers, when you mostly need to send data once a day/week/month in a large batch always at the same time, and when the data exchanged only need to go to one place.
However, there are as well very valid limitations to this model:
- Lack of scalability and fault tolerance. For example, an overloaded receiver could start running slower, or lose connections leading to delay in processing or worse, message loss.
- There are no economies of scale. If (when) 2 receivers are interested in the same information, the integration needs to be duplicated either by the sender, or by one of the receivers to be then forked to the other, or in a staging area which adds another point of failure (and defeats one of the “pro traditional” arguments). All these duplications cause compounded complexity.
- Sender and receiver are tied by a contract (a.k.a. the data schema) that cannot be changed unless both agree on the terms.
Through these limitations, you can see how 2 systems interfaced using this model are tightly coupled and one change in one system may require intervention of all parties involved.
Another consequence: because of the lack of governance that this model entails, all these disparate interfaces built overtime by different developers with different naming conventions start to weigh heavily on the support side, creating pockets of expertise. These pockets become a liability for an enterprise due to the amount of effort and training required to maintain knowledge.
When to use
I personally would not use Traditional Integration end-to-end. But, system limitations sometimes force the use of traditional integration as a step in an overall integration. For example: when a 3rd party customer or supplier is only able to send a file through [s]FTP. In this case, the receiver first task would be to decouple the flow as I will discuss in the next articles.
In the next article, I will discuss a first step in alleviating Limitation #1 above: Point-to-Point asynchronous messaging.
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