How do you manage data migration and conversion when changing your data model?

The paragraph above glosses over "destinations" implying they're a single output stream. However, most databases are integral to larger software processes that depend on data residing in certain tables, columns, or formats. Simply focusing on the data model and the actual conversion task is meaningless without addressing its massive potential for downstream impacts. If any processes are directly interacting with the database instead of using clear APIs, it is essential to understand how all of those processes will be affected by any proposed data model changes. Often, documentation is not current and comprehensive enough to immediately assess these impacts, so this step will require a LOT of communication and preparation.

Data migration and conversion, while transitioning to a new data model, require a strategic approach rooted in meticulous planning and robust testing. Start with a clear data mapping that aligns old and new models, identifying transformations needed. It's imperative to invest in a comprehensive testing regime, including dry runs, to ensure data integrity and to catch issues before the full-scale migration. Automation tools can streamline this process, but never underestimate the value of a thorough manual review. Post-migration, rigorous validation is crucial. A phased rollout can mitigate risk, allowing for iterative refinement. Always ensure that your team is ready for quick troubleshooting during and after the transition.

Unfortunately you need to test on full dataset as well, as there might be single row in few petabytes of data which is from some older application and is saved bypassing constraints. Luckily you usually can do clone of your production datum and test migration using full datum. If you do it inside same security peremiter as production, will get pretty accurate timestamps for real migration later. Just be careful to not let acutal users depend on such clone, when using more advanced techniques like traffic shadowing or mirroring.

Usually, at this stage you can relax, as you did play this through many times by now and eliminated all the bugs from testing.

Two parallel tracks of data optimization are involved in data migration and conversion When data is moved from standard tables to normalized tables, constraints, hidden indexes, and materialized views are created For data migration, database parallelism should be utilized efficiently to move large sets of data

Automate as much as possible, often you can store documentation as metadata inside database itself, so any changes do database are automatically updating the documentation itself as well, at same time.

- Modern laws and compliance policies may require that in process of data migration, no operator/system administrator involved will have any direct access to data entrusted to you by your customers. (as an example: If there is some record that does not "fit" into new system (say due to changed encoding) and gives you an error, your error message will need to expose just the bare minimum of characters that is errorneous for new system but does not allow you to tie it to any exact person or company.) - In some cases, where physical replication was used to move data around systems, indexes may silently stop returning some of the rows due to tiny changes in locale/collate betwen old and new systems.

Why has no one mentioned the use of a staging area, firstly to get the data away from the production area of the systems you are taking the data from, secondly to collate the data into manageable chunks to allow for proper inspection of the data to a common format.