By default all columns are string 50 in a Flat File Connection Manager. To get as many rows in a data flow buffer it's important to use a minimum data type for each column. And if it's for example an integer column then parse it to an integer in the Flat File Connection Manager instead of parsing it in the data flow with a Data Conversion or Derived Column Transformation. Otherwise you end up with two columns in your buffer instead of one.
A good start is to use the Suggest Types button in the Flat File Connection Manager editor. It will scan a couple of rows from your flat file and come up with a minimum data type.
In the Flat File Connection Manager it's impossible to skip columns that you don't need, but to minimize pressure on performance you should not parse unneeded columns. Parsing/converting is expensive. Just leave it string. In the Flat File Source editor you can uncheck the unneeded columns.
|Leave it string, don't parse.|
|Uncheck unneeded flat file columns|
If you have a so called 'trusted' source (for example a database export) then you don't have to worry for mistakes in the data types. To speed up the performance you can enable fast parse for all non-string columns. But for date columns you have to be sure that the format is correct. Try using the ISO format YYYY-MM-DD.
You can enable fast parse in the Advanced Editor of the Flat File Source. Right click it and choose "Show Advanced Editor...".
|Show Advanced Editor...|
Then go to the Input and Output Properties tab and then to the Output Columns. Select a non-string column and set the FastParse property to true. Repeat this for all non-string columns.
Bulk Insert Task
If your destination is a SQL Server table and you don't need data transformations then you might want to consider/test the Bulk Insert Task as a alternative for the Data Flow Task.
Minimum data types
Parse in Connection Manager
No unnecessary parsing
More info: Blog of Jamie Thomson and Henk van der Valk or MSDN