Big data question. how to generate a variable efficiently and aggregate

I have a file of tens of millions observations with a string identifier, which I load as a datastore:

1. ............. V1 ..... V2 ............ V3 ........ V4
2. # # * # KLM88 2001-06-30 10 COMPANY1
3. # # * # KLM88 2000-12-31 20 COMPANY1
4. # # * # MNH7C 2001-09-30 23 COMPANY1
5. # # * # MNH7C 2001-06-30 15 COMPANY1
6. # # * # MNH7C 2000-12-31 6 COMPANY1
7. # # * # HG9LB 2000-12-31 2 COMPANY1

I also have a mat file with some extra information and matching of first variable:

1. # KLM88 COUNTRYA
2. # MNH7C COUNTRYA
3. # HG9LB COUNTRYB

I wish for an end result such that I aggregate on country and date and company my dataset :

1. # * # 2001-09-30 23 COMPANY1 COUNTRYA
2. # * # 2001-06-30 25 COMPANY1 COUNTRYA
3. # * # 2000-12-31 26 COMPANY1 COUNTRYA
4. # * # HG9LB 2000-12-31 2 COMPANY1 COUNTRYB

I know I can do so by reading per dataChunk and with for loop assigning the country. However, that takes a huge amount of time. Any other suggestions of how to do so? I am fairly new to the concepts of tall arrays/ mapreduce etc. Thus, I am not sure how could I arrive to what I want more efficiently.

Answer 1

[Sean de Wolski]

Use the table join function.

https://www.mathworks.com/help/releases/R2017a/matlab/ref/join.html