The main goal is to design an Extreme near-data platform to enable consumption, mining and processing of dis- tributed and federated data without needing to master the logistics of data access across heterogeneous data locations and pools. We go beyond traditional passive or bulk data ingested from storage systems towards next generation near-data processing platforms both in the Cloud and in the Edge. In our platform, Extreme Data in- cludes both metadata and trustworthy data connectors enabling advanced data management operations like data discovery, mining, and filtering from heterogeneous data sources. The three core objectives are: O-1 Provide high-performance near-data processing for Extreme Data Types: The first objective is to create a novel intermediary data service (XtremeDataHub) providing serverless data connectors that optimize data management operations (partitioning, filtering, transformation, aggregation) and interactive queries (search, discovery, matching, multi-object queries) to efficiently present data to analytics platforms. Our data connectors facilitate a elas- tic data-driven process-then-compute paradigm which significantly reduces data communication on the data interconnect, ultimately resulting in higher overall data throughput. O-2 Support real-time video streams but also event streams that must be ingested and processed very fast to Object Storage: The second objective is to seamlessly combine streaming and batch data processing for analytics. To this end, we will develop stream data connectors deployed as stream operators offering very fast stateful computations over low-latency event and video streams. O-3 The third objective is to create a Data Broker service enabling trustworthy data sharing and confidential orchestration of data pipelines across the Compute Continuum. We will provide secure data orchestration, transfer, processing and access thanks to Trusted Execution Environments (TEEs) and federated learning architectures.