The National Data Warehouse (Universal infrastructure for storing and sharing data and effective processing of large volumes of data in HPC, BigData and artificial intelligence models) is a project included in the Map of Research Infrastructures of the Ministry of Science and Higher Education. R&D work and challenges emerging in the economy require the use of ever-increasing volumes of data. Thus, it becomes crucial to have large capacities for storing data as well as services enabling the use of such a large infrastructure. The overarching goal of the project is to create a national EOSC node connected in a federated environment by the European Commission's EOSC EU NODE data node and other national nodes of EU member states. It is also worth mentioning that the European node EOSC EU NODE is being built by the Project Leader, PSNC, who obtained the best rating among the applicants for this task in a public tender procedure. In addition, the Ministry of Science and Higher Education is interested in using the National Data Warehouse. KMD is included in the proposal for a document defining the open data policy as a response to the European Commission's EOSC (European Open Science Cloud) policy. It will be a tool enabling the publication of raw data obtained from scientific or research and development grants of the NCN (National Science Centre), NCBR (National Centre for Research and Development) and the Ministry of Science and Higher Education. In addition to high interest in the scientific community, there will also be challenges in the economy requiring the use of ever-increasing volumes of data. Thus, it becomes crucial to have large capacities for storing data. The dispersed nature of the infrastructure also means increased reliability of the services provided and their safety, so important in the area of business and economy. The involvement of KMD4EOSC project partners in the service of users and business institutions will be increased. Currently, the use of data science tools is growing exponentially, including Google Analytics and Google Analytics. Jupyter Notebooks both in science and the economy (e.g. for the so-called Jupyter Notebooks). Business Intelligence). Similarly, companies use artificial intelligence models very intensively to implement and optimize business processes and provide completely new applications and services. On the wave of these market opportunities, the tools and solutions provided and maintained by the project partners so far mainly for science and research purposes, including Data Science tools and learner data repositories and AI models implemented in the form of code, containers and other ready-made application environments, will be offered to a wide range of companies and monetized as commercial services. The project laboratories were organized into 4 activity areas related to the validation, implementation and availability of technological components and organizational solutions, relating to four levels of abstraction of technical solutions and activities for open data: infrastructure (physical level), repositories (logical level), durability mechanisms (technical and organisational level) and interfaces for access and processing and data analysis (application level). KMD4EOSC addresses FENG Priority 2 and Action 2.4 by creating a data platform that by its nature supports all economic areas requiring innovative solutions in their economic transformation related to increasing the level of security, reliability and the use of new technologies to build next-generation services in the next years of transformation, i.e. 2026 - 2030+. The economic aspect is one of the most important objectives of the project. This is further evidenced by the size of the economic part, which significantly exceeds the minimum requirement (30%) and amounts to 55% of the entire infrastructure and the size of development works. The non-economic part of the project is addressed in the KMD4EOSC 45% project. The specific objective of the project is also to improve the qualifications of the staff.