How to load my tables into Spark dataframes faster with Scala?

Question

I made a code that is supposed to load many tables (listed through the method LTables) into differents dataframes in Scala using Spark. Here is my code:

LTables.iterator.foreach{
  Table=> TableProcessor.execute(sparkSession,filterTenant,Table)

    if (Table.TableDf.count()>0) {
  GenerateCsv.execute(sparkSession, Table.TableDf,Table.OutputFilename, filterTenant)
    }
}

In my foreach loop, I process TableProcessor.execute that makes an SQL query and put the result into a dataframe and process a filtering, and then GenerateCsv just load filtered data into a csv.

The thing is, I have a lot of tables with large amount of data to process, so the full process is very slow (I tryed with a list of 160 tables) I know Spark is great to process a big dataframe and not that great to deal with a lot of dataframes, but I have to get tables separatly using SQL queries.

If you have solution or advice to help me make this code run faster, it would be great.

Thank's for helping