The "Big Bang" migration method is attractive for smaller data sets and workforces.
Some of the challenges include the problems associated with thoroughly testing all possible migration use cases for a larger data set. Due the volume of data it is often impossible, or at least very impractical to do a complete testing of the migrated data.
We often find that companies instead opt for only testing the top few most important use cases and data sets for accuracy, with the assumption that the rest of the data can be cleaned up later.
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Incremental Migrations are most successful when dealing with well compartmentalized data.
With an incremental Migration, groups of data, users, projects, are migrated in small chunks. This allows for more complete testing of smaller data volumes.
Incremental Migrations will take longer than a "Big Bang" migration. There also exists issues with interdependencies with legacy data that has yet to be migrated.
While more complex initially to set up, we find that incremental migrations tend to be more successful in the long run.
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