Data analytics is one of the main cornerstones in many enterprise architectures and the data lake paradigm is more and more adopted to assist organizations in taking reliable, accurate, and fast decisions. Although the initial approaches to address these issues saw the data lakes as the evolution of data warehouses to be implemented on-premises, cloud providers are nowadays including in their offerings platforms able to setup and run them. Nevertheless, the increasing amount of data generated at the edge and the need to enable the data sharing among organizations are posing new challenges in terms of performances, energy efficiency, and privacy/confidentiality which can be properly addressed with data lakes which are deployed along the whole computing continuum as well as building a federation of such data lakes. The ambition of TEADAL is to provide key cornerstone technologies to create stretched data lakes spanning the cloud-edge continuum and multi-cloud, providing privacy, confidentiality, and energy-efficient data management. The TEADAL data lake technologies will enable trusted, verifiable and energy efficient data flows, both in a stretched data lake and across a trustworthy mediatorless federation of them, based on a shared approach for defining, enforcing, and tracking privacy/confidentiality requirements balanced with the need for energy reduction.