From a schema point of view, when you split a column you actually create two new columns and drop the original one. If you deploy this change, any data in the dropped column will be lost.

To avoid this, you can write a migration script to copy the data to the new columns before dropping the original column. For a walkthrough of this process, see Splitting a column without data loss.

From a schema point of view, when you merge a column, you create a new column and drop the original columns. If you deploy this change, any data in the dropped columns will be lost. 

To avoid this, you can write a migration script to copy the data from the original columns to the new column before dropping the original columns.

You can follow the same steps for splitting or merging a tables as you would for splitting or merging columns. 

Splitting a table

Merging tables

If you add a NOT NULL constraint to a column in a table that contains NULL entries, without a default value, the deployment will fail.

Rather than adding a default value, you can write a migration script to update all the existing NULL entries with a NOT NULL value before adding the NOT NULL constraint to the column:

When you change a column's data type, data might be lost. For example, if the data type you change it to doesn't accommodate some of the rows, data will be truncated during deployment. 

To avoid this, you can create a migration script to appropriately modify rows that would otherwise be truncated.

When you rename a table in SQL Server Management Studio, this is interpreted by SQL Change Automation as dropping and recreating the table. If the table contains data, the data will be lost. 

To avoid this, generate a migration script for this change and replace the DROP and CREATE statements in the generated script with the sp_rename stored procedure.