A Tale of Two Data-Integration Technologies

Loraine Lawson

Integration appliances aren't a huge market. CastIron springs to mind, and Wikipedia offers a small list of vendors that includes IBM's DataPower and Intel's SOA Expressway, a name that demonstrates, yes, vendors will tack "SOA" onto anything.


You can certainly understand the appeal of appliances. Theoretically, you just plug it in and get to work. But you may also have reservations about how well these integration appliances work-are they really robust enough for your data integration needs? What about reporting requirements? Data quality?


If you need a low-cost and simple method of integration, you should be fine, but if you should probably pass if you're looking for something that can handle more heavy-duty problems, according to Timothy Leonard.


Leonard recently completed a global data warehouse using appliances in a hub-and-spoke design, and in a recent BeyeNetwork article, he shares his experience with integration appliances so that others can judge for themselves whether appliances will fit their needs. He evaluates integration appliances in three areas: volume, integration of data and extraction of data for reporting.


He determines that appliances definitely have an advantage both in terms of cost and flexibility when it comes to handling volumes of data, but when it comes to integration and reporting, appliances can be a disadvantage.


The appliance integration story is interesting, because, as Leonard points out, the appliance actually has an advantage on the front end of the data acquisition life cycle. After all-it is an integration appliance; it ships with integration capability.


But it's when you need to integrate more complex data that you hit a wall with appliances, he writes:

Appliances integrate data very quickly as long as the data integration business rules are simple. As soon as you start to add subject area after subject area, the process slows down. The bottleneck tends to happen around the integration layer component where data integration, separation and transformation are handled.

Since this blog focuses primarily on integration, I'll talk more about that and let you read the full article for the details on volume and reporting.


If integration appliances aren't your thing, you might be interested to learn more about how data virtualization tools can be used for integration. In a recent eBizQ article, Composite's vice president of marketing, Robert Eve, explains how data virtualization can be used for integration, as a data foundation for mashups and even as extensions for data warehouses. He discusses eight data virtualization usage patterns in all.


As I've mentioned before, Eve is the only person I know who regularly writes about the application of data virtualization to data integration, and I'm not sure why. I think it may be an issue of naming, since several of the approaches he discusses sound similar to the concept of data services and some styles of master data management.

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