The surge in demand for cost-effective long-term archival media, coupled with density limitations of contemporary magnetic media, has resulted in synthetic DNA emerging as a promising new alternative. Despite its benefits, storing data in DNA poses several challenges as the technology used for reading/writing data on DNA are highly error prone. Thus, it is important to design pipelines that can efficiently use redundancy to mask errors without amplifying read/write cost. In this work, we present Columnar MOlecular Storage System (CMOSS), a novel, end-to-end DNA storage pipeline that can provide error-tolerant data storage at low read/write costs. CMOSS differs from state-of-the-art (SOTA) on three fronts (i) a motif-based, vertical layout in contrast to nucleotide-based horizontal layout, (ii) integrated consensus calling and decoding enabled by the vertical layout, and (iii) a flexible, block-based data organization for random access over DNA storage in contrast to object-based organization. Using an in-depth evaluation with several simulated and real wet lab experiments, we demonstrate the benefits of CMOSS design.

Given the growing adoption of AI, cloud data lakes are facing the need to support cost-effective "just-in-case" data archival over long time periods to meet regulatory compliance requirements. Unfortunately, current media technologies suffer from fundamental issues that will soon, if not already, make cost-effective data archival infeasible. In this paper, we present a vision for redesigning the archival tier of cloud data lakes based on a novel, obsolescence-free storage medium-synthetic DNA. In doing so, we make two contributions: (i) we highlight the challenges in using DNA for data archival and list several open research problems, (ii) we outline OligoArchive-DSM (OA-DSM)-an end-to-end DNA storage pipeline that we are developing to demonstrate the feasibility of our vision.

Modern server hardware is increasingly heterogeneous with a diverse mix of XPU architectures deployed across CPU, GPU, and FPGAs. However, till date, database developers have had to rely on either proprietary, architecture-specific solutions (like CUDA), or low-level, cross-architecture solutions that complicate development (like OpenCL). The lack of portable parallelism caused by the absence of a common high-level programming framework is one of the main reasons preventing a wider adoption of XPUs by database systems.