Connecting A to B: APIs or data formats?

All successful examples of interconnecting A and B in EAI feature both "how" and "what". Unfortunately, the two can get terribly inter-twined leading to integration strategies that are difficult to develop, deploy and debug.

A useful pattern exists for combining the "how" with the "what" in a way that keeps the two separate and leads to clean, manageable integrations. It is best illustrated with a worked example.

Imagine that system A has some data that needs to be regularly sent to system B. System B has an API that can be used to send it data -- i.e. it allows the development of custom client applications. The obvious first thing to do is to program right to this API and thereby inject the data from A directly into B. The trouble with this obvious approach is that A and B end up temporally coupled.

This is fine and everything will work fine as long as (a) system B is never down for maintenance, (b) system B never slows down to the point where your injecting program times out and (c) there are never debugging nightmares in which A says it has sent something, B claims not to have got it and you are sitting in the middle scratching your head...

Temporal coupling bites and when it bites it bites hard.

Let us go back to basics for a moment. A sends data to B. Okay. We can split the problem by pumping the data out of A into some persisted file format. We can develop and debug this piece standalone - without having a B running. Now the problem reduces to injecting into B - not from A - but from the data previously serialized out of A. And guess what, by being clever about it, we might find we don't have to inject at all! It could be that we can target a file format that B already understands and use an existing "import" facility to load it up. Now we have successfully sent data from A to B in two, independently debuggable hops.

Now let's imagine that both hops are working fine and we want to go the whole hog - making the two hops behave as one. We write data out of A as before but now, at the end of that process, we also use B's API to invoke the data import into B. We achieve the full effect of seamless integration from A to B but with some major benefits.

Common scenario. A is ready to send data to B but darn it, B is down at the moment! What to do? Just send the data to disk and queue it up. When B comes online, do the import. A can go on about its business.

Common scenario. A says the data was sent to B but there is no sign of it in B. Start your diagnostics by looking at the data serialized out of A. If all is well there on the disk then the problem is downstream of A. If not, the problem is in A. There are few things more frustrating than bug reports that claim A is broken when the problem really is B and visa versa.

Common scenario. You wish to stress test A's ability to output a gazillion files for B. By targeting the data exported from A, you can stress test and validate all of A's processing independently of B. The reverse is also true.

Common scenario. B has changed and now the integration with A is no longer working. Management are worried because there is no planned future development of A. It is an unwitting victim of the way B was upgraded. With the data-centric approach, it is often possible to leave the A part alone, transform the data exported from A perhaps and just change the B side. It needs to change anyway because B has changed.

The moral of the story is this: just because you can envision a single hop strategy to integrating A and B does not mean that it is best implemented as a single hop.