The computing continuum can enable new, novel big data use cases across the edge-cloud-supercomputer spectrum. Fast and high-volume data movement workflows rely on state-of-the-art architectures built on top of stream ingestion and file transfer open-source tools. Unfortunately, users struggle when faced with dealing with such diverse architectures: stream ingestion was designed for small-size datasets and low latency, while file transfer was designed for large-size datasets and high throughput. In this paper, we propose to unify ingestion and transfer, while introducing architectural design principles and discussing future implementation challenges.

The huge volume, variety, and velocity of big data have empowered Machine Learning (ML) techniques and Artificial Intelligence (AI) systems. However, the vast portion of data used to train AI systems is sensitive information. Hence, any vulnerability has a potentially disastrous impact on privacy aspects and security issues. Nevertheless, the increased demands for high-quality AI from governments and companies require the utilization of big data in the systems. Several studies have highlighted the threats of big data on different platforms and the countermeasures to reduce the risks caused by attacks. In this paper, we provide an overview of the existing threats which violate privacy aspects and security issues inflicted by big data as a primary driving force within the AI/ML workflow. We define an adversarial model to investigate the attacks. Additionally, we analyze and summarize the defense strategies and countermeasures of these attacks. Furthermore, due to the impact of AI systems in the market and the vast majority of business sectors, we also investigate Standards Developing Organizations (SDOs) that are actively involved in providing guidelines to protect the privacy and ensure the security of big data and AI systems. Our far-reaching goal is to bridge the research and standardization frame to increase the consistency and efficiency of AI systems developments guaranteeing customer satisfaction while transferring a high degree of trustworthiness.