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| Formáid: | Recurso digital |
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Zenodo
2020
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| Ábhair: | |
| Rochtain ar líne: | https://doi.org/10.5281/zenodo.16631989 |
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| _version_ | 1866901830045270016 |
|---|---|
| author | Naga Surya Teja Thallam |
| author_facet | Naga Surya Teja Thallam |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16631989 |
| institution | Zenodo |
| language | |
| publishDate | 2020 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Comparative Analysis of Data Warehousing Solutions: AWS Redshift vs. Snowflake vs. Google BigQuery Naga Surya Teja Thallam Cloud Data Warehousing AWS Redshift, Snowflake Google BigQuery Performance Benchmarking Security Data Storage Efficiency <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> |
| title | Comparative Analysis of Data Warehousing Solutions: AWS Redshift vs. Snowflake vs. Google BigQuery |
| topic | Cloud Data Warehousing AWS Redshift, Snowflake Google BigQuery Performance Benchmarking Security Data Storage Efficiency |
| url | https://doi.org/10.5281/zenodo.16631989 |