This paper argues that the data warehouse architecture as we know it today will wither in the
coming years and be replaced by a new architectural pattern, the Lakehouse, which will (i) …

Deep learning (DL) training jobs bring some unique challenges to existing cluster
managers, such as unpredictable training times, an all-or-nothing execution model, and …

Big data has revolutionized science and technology leading to the transformation of our
societies. High-performance computing (HPC) provides the necessary computational power …

Recent workload trends indicate rapid growth in the deployment of machine learning,
genomics and scientific workloads on cloud computing infrastructure. However, efficiently …

Data centers are widely used for big data analytics, which often involve data-parallel jobs,
including query and web service. Meanwhile, cluster frameworks are rapidly developed for …

Growing main memory capacity has fueled the development of in-memory big data
management and processing. By eliminating disk I/O bottleneck, it is now possible to support …

Low latency analytics on geographically distributed datasets (across datacenters, edge
clusters) is an upcoming and increasingly important challenge. The dominant approach of …

Communication in data-parallel applications often involves a collection of parallel flows.
Traditional techniques to optimize flow-level metrics do not perform well in optimizing such …

There has been much research devoted to improving the performance of data analytics
frameworks, but comparatively little effort has been spent systematically identifying the …

Small jobs, that are typically run for interactive data analyses in datacenters, continue to be
plagued by disproportionately long-running tasks called stragglers. In the production …