Organizations across industries increasingly rely on sharing data to drive collaboration, innovation, and business performance. However, securely and efficiently sharing live data across diverse platforms and adhering to varying governance requirements remains a significant challenge. Traditional approaches, such as FTP and proprietary in-data-warehouse solutions, often fail to meet the demands of interoperability, cost, scalability, and low overhead. This paper introduces Delta Sharing, an open protocol we developed in collaboration with industry partners, to overcome these limitations. Delta Sharing leverages open formats like Delta Lake and Apache Parquet alongside simple HTTP APIs to enable seamless, secure, and live data sharing across heterogeneous systems. Since its launch in 2021, Delta Sharing has been adopted by over 4000 enterprises and supported by hundreds of major software and data vendors. We discuss the key challenges in developing Delta Sharing and how our design addresses them. We also present, to our knowledge, the first large-scale study of production data sharing workloads offering insights into this emerging data platform capability.

Overlay multicast streaming is built out of loosely coupled end-hosts (peers) that contribute resources to stream media to other peers. Peers, however, can be malicious. They may intentionally wish to disrupt the multicast service or cause confusions to other peers. We propose two new schemes to detect malicious peers in overlay multicast streaming. These schemes compute a level of trust for each peer in the network. Peers with a trust value below a threshold are considered to be malicious. Results from our simulations indicate that the proposed schemes can detect malicious peers with medium to high accuracy, depending on cheating patterns and malicious peer percentages