from concurrent.futures import ThreadPoolExecutor

def process_item(item):
    # Simulate a time-consuming task
    import time
    time.sleep(0.1)
    return item * 2

def generate_data():
    for i in range(10):
        yield i

def process_data_in_parallel(data_generator):
    with ThreadPoolExecutor(max_workers=4) as executor:
        results = executor.map(process_item, data_generator)
        return list(results)

# Usage example
data_generator = generate_data()
processed_data = process_data_in_parallel(data_generator)
print(processed_data)