SmartDataLake: Distributed Analytics over Heterogeneous Data

This lecture discusses the challenges in scalable analytics over heterogeneous Big Data and the limitations of existing platforms like Spark. It introduces SmartDataLake, a platform aiming to handle raw, heterogeneous data efficiently by leveraging existing platforms and extending them to support native handling and distributed analytics. The lecture covers topics such as data distribution, adaptive scheduling, data prefetching, and automated storage tiering. SmartDataLake's design focuses on optimizing task distribution, resource allocation, and query execution to improve performance and reduce latency. The lecture also highlights the importance of storage tiering, approximate analytics, and the potential for cost-effective scalability. Various industry-standard benchmarks will be used to validate SmartDataLake's capabilities.