Data Management for Data Science  Towards Embedded Analytics The rise of Data Science has caused an influx of new usersin need of data management solutions.  However,  insteadof utilizing existing RDBMS solutions they are opting touse a stack of independent solutions for data storage andprocessing glued together by scripting languages.  This is notbecause they do not need the functionality that an integratedRDBMS provides, but rather because existing RDBMS implementations do not cater to their use case.  To solve theseissues, we propose a new class of data management systems:embedded  analytical  systems.   These  systems  are  tightlyintegrated with analytical tools, and provide fast and efficient access to the data stored within them.  In this work,we describe the unique challenges and opportunities w.r.tworkloads, resilience and cooperation that are faced by thisnew class of systems and the steps we have taken towardsaddressing them in the DuckDB system.

Analysis in the Data Path of an ObjectCentric Data Management System Emerging high performance computing (HPC) systems are expected to be deployed with an unprecedented level of complexity due to a deep system memory and storage hierarchy. Efficient and scalable methods of data management and movement through the multilevel storage hierarchy of upcoming HPC systems will be critical for scientific applications at exascale. In this paper, we propose in locus analysis that allows registering userdefined functions (UDFs) and running those functions automatically while the data is moving between levels of a storage hierarchy. We implement this analysis in the data path approach in our objectcentric data management system, called Proactive Data Containers (PDC). The transparent invocation of analysis functions as part of PDC object mapping is an optimized approach to minimize latency to access data as it moves within the storage hierarchy. Because a user defined analysis or transform function will be invoked automatically by the PDC runtime, the user simply registers their functions for PDC to identify the function name as well as the required list of actual parameters. To demonstrate the validity and flexibility of this analysis approach, we have implemented several scientific analysis kernels to compare against other HPC analysisoriented approaches.