The Best Methodology to Ensure A Successful Data Migration
Data Migration is one of the most challenging tasks for the companies. They spend millions on data migrating between information-intensive applications. Main challenge is the ability to validate certain number of tables containing large number of data. While this process is done, there can be a great loss of data along with data corruption. Hence, along with the process, testing is also necessary. There can be various methodologies to follow to have a successful Data migration management process. Here, in this article, I have mentioned one of the finest method stepwise to make sure a successful data migration process. The validation tests required to ensure the success of the entire process of data migration.
Validation with Schema
This is one of the most necessary process during the data migration. Data regarding stored procedures, views, or user preferences are the part of data migration. But doing manually is very time consuming, and hence it is necessary to automate the process simply by writing a script that would let you know about the mismatch that exists between source and the target. When you automate the process, the metadata can be compared and the mismatches can be easily found.

Reconciliation Checks Are Necessary
This is one of the most important stage of testing in Data migration as it checks whether the data is corrupted or not, maintenance of date format and also checks whether the data is loaded completely or not. As mentioned earlier, due to the size of the data, manual validations are not possible and the manual checks of missing columns can make you remove the columns which will then change the structure of data. Hence, automation is necessary.
So, in order to automate the process, all you need is a Unix shell script which will create the entire list of the tables and then will export the text file as a parameter to the shell. The working of the script is to pick up the numeric columns from the table and create post scripts. After which it will execute those and store that in the file. You must make sure that these steps are performed on both source and target database. After the completion, both the files can be compared and are generated on the source and target database in order to identify the mismatches.
Null Validation
This validation is required to check if NULL is not inserted accidentally at any place instead of valid data. Automating this process will result into the similar results as in other validations. All you need to do is to write a script to validate the count of useful and unuseful NULL functions between source and target databases.
Ad Hoc Testing
This can be the final step in the testing process. This testing makes sure that the target business application and the data that is recently migrated are able to successfully process the data. This process include running a business query from customer to make sure that the data is migrated successfully.
Be Careful while migrating the data and make sure that in the Data migration management process, none of the data is lost or corrupted.
