Efficient Analytics: Adapting to Modern Data Challenges

This lecture discusses the challenges of enabling efficient and scalable analytics in the context of modern hardware and data growth. The instructor presents their research focused on adapting high-performance systems to leverage the features of contemporary hardware. They emphasize the importance of workload adaptation, particularly concerning memory hierarchy and computational heterogeneity. The lecture highlights the role of approximate pre-processing and various sampling methods tailored for modern systems, which facilitate interactive and low-overhead execution. The instructor explores holistic optimization strategies, including hardware-conscious algorithms and adaptive methods for data exploration. They also address the need for runtime adaptivity to manage the complexities of hardware, data, and workload. The discussion extends to the evolution of analytics beyond traditional relational tables, advocating for hybrid machine learning relational systems that encapsulate complexity and enhance efficiency. The lecture concludes by acknowledging the exciting challenges in data management and the necessity for systems to be flexible and adaptive to remain efficient and scalable.