  The exponential growth of artificial intelligence (AI) and machine learning
(ML) applications has necessitated the development of efficient storage
solutions for vector and tensor data. This paper presents a novel approach for
tensor storage in a Lakehouse architecture using Delta Lake. By adopting the
multidimensional array storage strategy from array databases and sparse
encoding methods to Delta Lake tables, experiments show that this approach has
demonstrated notable improvements in both space and time efficiencies when
compared to traditional serialization of tensors. These results provide
valuable insights for the development and implementation of optimized vector
and tensor storage solutions in data-intensive applications, contributing to
the evolution of efficient data management practices in AI and ML domains in
cloud-native environments
