Annotation: The choice of a model for representing data and knowledge in storage systems determines several of their simultaneously unattainable most important characteristics, including: the ability to create various flexible and complex data and knowledge architectures required by users, simplicity and convenience of working with them, memory efficiency, and access speed to data and knowledge. In each case, one of the compromise solutions is used, often proposed many decades ago and widely used. The authors propose to change the existing compromises and move on to using new models for representing data and knowledge. It is important to understand that new models are proposed under the conditions of only a partially formed set of evaluation criteria, the lack of information and research on this topic, and in the absence of formalized rules and procedures for choosing a model. The purpose of the work is to expand the capabilities of data and knowledge storage systems to create a variety of flexible and complex data and knowledge architectures as well as to improve the efficiency of memory use. The article provides an overview of the main types of data and knowledge storage systems, showing their shortcomings. The method of organization based on the archigraph model for each type of system is proposed, allowing to eliminate the identified shortcomings. The proposed solutions can be used to create new file systems, database management systems, data warehouse management systems, data lake and knowledge lake management systems, management systems for Data Lakehouse, Data Space, Data Fabric, Data Mesh architectures, and blockchain platforms

Keywords: archigraph, file system, database management system, data lake, Data Lakehouse, Data Space, Data Fabric, Data Mesh, knowledge lake