Heterogeneous datasets hold a large percentage of all digital data that is available. With the rise of the digital medium, they have played a strong part in addressing the need for a structured way of storing data, particularly through the use of relational databases. To better leverage such data, the consensus of researchers has been in favour of using Graph Neural Networks to make predictions and infer pos- sible outcomes. With the rise of the Transformer model and the clear limitations that GNNs inherently have due to their over-smoothing and over-squashing properties, a clear transition occurred into combining and leveraging the properties of both GNNs and Transformer models, Graph Transformers. While this method has been better researched in the context of homogeneous datasets, much remains unexplored in its heterogeneous counterpart. This paper tackles this by applying Graph Transformers to multiple heterogeneous datasets, examining the differences and advantages between interleaved and cascade architectures of GTs and how the homogeneous positional encodings transfer to the heterogeneous context.