Sql-server – Import poorly formatted CSV/JSON to SQL Server

At the end of the import/export wizard you have the option to save your SSIS package (yes that's what the wizard uses behind the scenes). Save your package to a file then open it in SQL Server Data Tools. Go into the data flow and open the source. In the "Error Output" tab you will see an option to ignore failure (for truncation specifically or errors in general). Modify it for the columns you are worried about.

All that being said you are probably(99.999%) not seeing a truncation because the column is to big to fit in a varchar(max). What is probably happening is that SSIS "thinks" your column is shorter than it is. If you go into the connection manager for your CSV file you can modify the "width" of your input columns in the advanced tab.

In the end I coded a Python function import_csv_to_dynamodb(table_name, csv_file_name, colunm_names, column_types) that imports a CSV into a DynamoDB table. Column names and column must be specified. It uses boto, and takes a lot of inspiration from this gist. Below is the function as well as a demo (main()) and the CSV file used. Tested on Windows 7 x64 with Python 2.7.5, but it should work on any OS that has boto and Python.

import boto

MY_ACCESS_KEY_ID = 'copy your access key ID here'
MY_SECRET_ACCESS_KEY = 'copy your secrete access key here'


def do_batch_write(items, table_name, dynamodb_table, dynamodb_conn):
    '''
    From https://gist.github.com/griggheo/2698152#file-gistfile1-py-L31
    '''
    batch_list = dynamodb_conn.new_batch_write_list()
    batch_list.add_batch(dynamodb_table, puts=items)
    while True:
        response = dynamodb_conn.batch_write_item(batch_list)
        unprocessed = response.get('UnprocessedItems', None)
        if not unprocessed:
            break
        batch_list = dynamodb_conn.new_batch_write_list()
        unprocessed_list = unprocessed[table_name]
        items = []
        for u in unprocessed_list:
            item_attr = u['PutRequest']['Item']
            item = dynamodb_table.new_item(
                    attrs=item_attr
            )
            items.append(item)
        batch_list.add_batch(dynamodb_table, puts=items)


def import_csv_to_dynamodb(table_name, csv_file_name, colunm_names, column_types):
    '''
    Import a CSV file to a DynamoDB table
    '''        
    dynamodb_conn = boto.connect_dynamodb(aws_access_key_id=MY_ACCESS_KEY_ID, aws_secret_access_key=MY_SECRET_ACCESS_KEY)
    dynamodb_table = dynamodb_conn.get_table(table_name)     
    BATCH_COUNT = 2 # 25 is the maximum batch size for Amazon DynamoDB

    items = []

    count = 0
    csv_file = open(csv_file_name, 'r')
    for cur_line in csv_file:
        count += 1
        cur_line = cur_line.strip().split(',')

        row = {}
        for colunm_number, colunm_name in enumerate(colunm_names):
            row[colunm_name] = column_types[colunm_number](cur_line[colunm_number])

        item = dynamodb_table.new_item(
                    attrs=row
            )           
        items.append(item)

        if count % BATCH_COUNT == 0:
            print 'batch write start ... ', 
            do_batch_write(items, table_name, dynamodb_table, dynamodb_conn)
            items = []
            print 'batch done! (row number: ' + str(count) + ')'

    # flush remaining items, if any
    if len(items) > 0: 
        do_batch_write(items, table_name, dynamodb_table, dynamodb_conn)


    csv_file.close() 


def main():
    '''
    Demonstration of the use of import_csv_to_dynamodb()
    We assume the existence of a table named `test_persons`, with
    - Last_name as primary hash key (type: string)
    - First_name as primary range key (type: string)
    '''
    colunm_names = 'Last_name First_name'.split()
    table_name = 'test_persons'
    csv_file_name = 'test.csv'
    column_types = [str, str]
    import_csv_to_dynamodb(table_name, csv_file_name, colunm_names, column_types)


if __name__ == "__main__":
    main()
    #cProfile.run('main()') # if you want to do some profiling

test.csv's content (must be located in the same folder as the Python script):

John,Doe
Bob,Smith
Alice,Lee
Foo,Bar
a,b
c,d
e,f
g,h
i,j
j,l