Scalable Cloud Data Warehousing Models For Global Logistics Tracking Systems
Abstract
Logistics tracking systems involving complex global distribution networks emit massive amounts of data that are typically stored in on-premises data warehouses followed by an export to a data mart for analytical consumption. Analytical operations are also often Monte Carlo simulations, sensitive to how closely the empirical data distribution matches the real-world distribution. Newer capabilities of cloud-based data warehouses can provide better scalability, elastic execution environments, and routing or geospatial capabilities with significantly reduced operational overhead.
Three data warehousing aspects common in traditional designs pursued by cloud-based data analytics of captured logistical shipment data over global tracking routes are analysed with respect to a data warehouse model supporting both real-time or near-real-time workloads: (a) multi-tenancy of the data warehouse schema, (b) data modeling features supporting these analytics, especially the temporal dimension, and (c) additional techniques supporting a global-scale query workload of such a model, especially the ability to elastically scale compute resources for query execution. Results suggest that given the massive scale of global tracking workloads, a design employing a multi-tenant schema configuration is reasonable if not essential to maintain a reasonable cost for operating the data warehouse.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 5 No. 6 (2022): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
8913-8929 |
Published |
December 8, 2022 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Vinod Battapothu (%2022). Scalable Cloud Data Warehousing Models For Global Logistics Tracking Systems. International Journal of Science, Research and Technology , Vol. 5 No. 6 (2022): International Journal of Science, Research and Technology (IJSRAT) , pp. 8913-8929. https://doi.org/10.15662/IJSRAT.2022.0506006 |
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