Αποθηκεύτηκε σε:
| Κύριος συγγραφέας: | |
|---|---|
| Μορφή: | Recurso digital |
| Γλώσσα: | |
| Έκδοση: |
Zenodo
2020
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| Θέματα: | |
| Διαθέσιμο Online: | https://doi.org/10.5281/zenodo.16631989 |
| Ετικέτες: |
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Πίνακας περιεχομένων:
- <p><span lang="EN-GB">This research is geared towards the experimental analysis of shock wave behaviour and its properties when with the increase in data that modern businesses are experiencing, it has become necessary to provide scalable, low cost and high-performance data warehousing systems that can tap into this potential. Some of the most popular cloud-based data warehouses includes, Amazon Redshift, Snowflake and Google BigQuery, each has a different architecture, performance optimizations, various pricing models and scalability mechanisms. In this paper, we provide a holistic comparison of these three platforms against several other dimensions such as architecture, query performance, storage efficiency, concurrency handling, cost structure, security mechanisms, scalability, etc. Their relative performance under different workloads is measured by performing a detailed empirical evaluation using standard benchmarking datasets. Cost function and theoretical models are developed to predict cost effiency with scale. Moreover, ease of integration, operational complexity, and vendor lock in risks are discussed from practical point of view. The main results show important performance, cost, and flexibility trade-offs that are important to enterprises selecting a suitable data warehousing solution according to its load characteristics and business requirements.</span></p>