The Transformer Architecture fundamentally changed the world of sequential data processing through its attention mechanism, allowing for greater parallelization during the data processing. Traditional models like recurrent neural networks process data sequentially, which can be time-consuming. Transformers eschew this limitation by using self-attention to weigh the significance of different parts of the input data. This makes them extremely powerful for tasks like language translation, where understanding context over long text sequences is crucial.