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We present the first global-scale database of 4.3 billion P- and S-wave picks extracted from 1.3 PB continuous seismic data via a cloud-native workflow. Using cloud computing services on Amazon Web Services, we launched ~145,000 containerized jobs on continuous records from 47,354 stations spanning 2002-2025, completing in under three days. Phase arrivals were identified with a deep learning model, PhaseNet, through an open-source Python ecosystem for deep learning, SeisBench. To visualize and gain a global understanding of these picks, we present preliminary results about pick time series revealing Omori-law aftershock decay, seasonal variations linked to noise levels, and dense regional coverage that will enhance earthquake catalogs and machine-learning datasets. We provide all picks in a publicly queryable database, providing a powerful resource for researchers studying seismicity around the world. This report provides insights into the database and the underlying workflow, demonstrating the feasibility of petabyte-scale seismic data mining on the cloud and of providing intelligent data products to the community in an automated manner. 

SUMMARY Seismology has entered the petabyte era, driven by decades of continuous recordings of broad-band networks, the increase in nodal seismic experiments and the recent emergence of distributed acoustic sensing (DAS). This review explains how cloud platforms, by providing object storage, elastic compute and managed data bases, enable researchers to ‘bring the code to the data,’ thereby providing a scalable option to overcome traditional HPC solutions’ bandwidth and capacity limitations. After literature reviews of cloud concepts and their research applications in seismology, we illustrate the capacities of cloud-native workflows using two canonical end-to-end demonstrations: (1) ambient noise seismology that calculates cross-correlation functions at scale, and (2) earthquake detection and phase picking. Both workflows utilize Amazon Web Services, a commercial cloud platform for streaming I/O and provenance, demonstrating that cloud throughput can rival on-premises HPC at comparable costs, scanning 100 TBs to 1.3 PBs of seismic data in a few hours or days of processing. The review also discusses research and education initiatives, the reproducibility benefits of containers and cost pitfalls (e.g. egress, I/O fees) of energy-intensive seismological research computing. While designing cloud pipelines remains non-trivial, partnerships with research software engineers enable converting domain code into scalable, automated and environmentally conscious solutions for next-generation seismology. We also outline where cloud resources fall short of specialized HPC—most notably for tightly coupled petascale simulations and long-term, PB-scale archives—so that practitioners can make informed, cost-effective choices.