SQL Server migration from denormalized to normalized table causing performance issues

We have a denormalized table in our SQL Server data model that we want to migrate to a normalized format. However, the query we have created for this migration is causing a significant delay. We have checked the actual execution plan and found that the estimated row count is much higher than the actual row count.

Old Table Data Model:

Column A (PK Column): Long
Column1: String
Column2: String
Column3: String

New table data model:

Column A (PK Column): Long
Column1: Long (FK value by referencing another table)
Column2: Long (FK value by referencing another table)
Column3: Long (FK value by referencing another table)

insert into newTable (pkcolumn, column1, column2, column3)
select 
    pkColumn,
    anothertable1.id,
    anothertable2.id,
    anothertable3.id
left join 
    anothertable as anothertable1 on anothertable1.field_id = 1 
                                  and anothertable1.value = oldtable.column1 
                                  and oldtable.pkColumn in (1,2,3,4,5,6,7,8,9,10)
left join 
    anothertable as anothertable2 on anothertable2.field_id = 2 
                                  and anothertable2.value = oldtable.column2 
                                  and oldtable.pkColumn in (1,2,3,4,5,6,7,8,9,10)
left join 
    anothertable as anothertable3 on anothertable3.field_id = 3 
                                  and anothertable3.value = oldtable.column3 
                                  and oldtable.pkColumn in (1,2,3,4,5,6,7,8,9,10)

Batch size: oldtable.pkColumn in (1,2,3,4,5,6,7,8,9,10....1000)

Problem

The query we have created for the migration from the denormalized to the normalized table is causing a significant delay. We have checked the actual execution plan and found that the estimated row count is much higher than the actual row count. We have already updated the statistics of the tables involved in the query and rebuilt the indexes of the column used in the query.

Question

Is there any way to increase the performance of the migration in this scenario?

Additional information:

Query Plan: Link

Based on your answer that the denormalized tables have 250 columns and 0.5 million rows each, in my opinion any index design and trying to do it in a single SQL statement will be difficult. So your options are

1. Run it in programmatic batch way in SQL - with proper index and statistics if it is managable and source data is static

2. This is my choice - use an ETL tool - to do this - or even use spark/databricks or if this is in cloud any cloud based tool

First thing, you can create FK after having migrates the data. This will reduce the speed.

Second, you can rewrite your JOINs as :

ON anothertable1.field_id = 1 
   and anothertable1.value = oldtable.column1 
   and oldtable.pkColumn BETWEEN 1 AND 10

But I did not see any normalization of your table.

This seems to me like this post :

Best index for table with dynamic predicate
Which I already answer