ELT pipelines that take BOOST coded microdata for each country, clean, harmonize, then store them in a single Delta table. Aggregated budget and expenditure data at the following levels are also created for visualization in PowerBI:

• by year by country
• by year by country by adm1

• Create a new folder with the country name in CamelCase
• Code & test the ELT scripts in databricks (set up DLT workflow if necessary)
• Add the script/DLT workflow as steps to the "BOOST Harmonize" Job (Workflows > Jobs)
• Add ELT pipeline for the country's adm1 level subnational population to mega-indicator repo – because it's a public good by itself.
• Add the intermediate table (containing only new country's cleaned microdata) as a source for stacking to cross_country_aggregate_dlt.py
• Check the PowerBI report to ensure the new country is reflected.
• If not all its adm1 areas are present in the map, check adm1_name are aligned in the subnational population dataset and the BOOST dataset.
• Check that the by year by country expenditure matches the aggregated data in BOOST source file