The legacy architecture of some organizations data landscape may need a serious upgrade to stay competitive. As the data volume, diverse data sources and integration complexity grows, data engineers need to invest a significant amount of time maintaining legacy data pipelines, hence they are unable to focus on new requirements and support AI/ML initiatives.