The comparative analysis of modern ETL tools
Article Sidebar
Open full text
Issue Vol. 19 (2021)
-
Comparison of WebSocket and HTTP protocol performance
Wojciech Łasocha, Marcin Badurowicz67-74
-
Comparative analysis of JavaScript package managers - yarn and npm
Michał Chodorowski75-80
-
Accessibility assessment of selected university websites
Wojciech Stasiak, Mariusz Dzieńkowski81-88
-
REST and GraphQL comparative analysis
Piotr Margański, Beata Pańczyk89-94
-
Comparative analysis of performance of ASP.NET Core MVC and Symfony 4 programming frameworks
Marcin Górski, Wojciech Andrzej Piwowarski, Mariusz Dzieńkowski95-99
-
Comparative analysis of frameworks used in automated testing on example of TestNG and WebdriverIO
Alla Shtokal, Jakub Smołka100-106
-
A multi-criteria comparison of mobile applications built with the use of Android and Flutter Software Development Kits
Damian Gałan, Konrad Fisz, Piotr Kopniak107-113
-
Evaluation of the availability of websites of communes in the Lubelskie Province
Michał Bednarczyk, Mariusz Dzieńkowski114-120
-
REST API performance comparison of web applications based on JavaScript programming frameworks
Marcin Grudniak, Mariusz Dzieńkowski121-125
-
The comparative analysis of modern ETL tools
Vitalii Mayuk, Ivan Falchuk, Piotr Muryjas126-131
-
Compilation of iOS frameworks from Linux operating system using open- source tools
Łukasz Rutkowski, Piotr Kopniak132-138
-
Performance analysis of Svelte and Angular applications
Gabriel Białecki, Beata Pańczyk139-143
-
Model of the text classification system using fuzzy sets
Dmytro Salahor, Jakub Smołka144-150
-
Analysis of the possibilities of optimizing SQL queries
Piotr Rymarski, Grzegorz Kozieł151-158
-
Comparison of lightweight frameworks for Java by analyzing proprietary web applications
Michał Błaszczyk, Marek Pucek, Piotr Kopniak159-164
Main Article Content
DOI
Authors
Abstract
Each data warehouse requires loading properly processed transactional data. The process that performs this task is known as extract-transform-load (ETL). The efficiency of its implementation affects how quickly the user will have the access to the current analytical data. The paper presents the results of research efficiency of ETL performance of its stage with the use of Azure Synapse (AS) and Azure Data Factory (ADF). The research included selection, sorting and aggregating data, joining tables, and loading data into target tables. To evaluate the efficiency of these operations, the criterion of their execution time has been used. The obtained results indicate that the ADF tool provides a much higher time efficiency of loading transactional data into the data warehouse comparing to AS.
Keywords:
References
Ł. Bielak, P. Muryjas, Integracja Big Data i Business Intelligence jako innowacyjne rozwiązanie wspomagające funkcjonowanie nowoczesnych organizacji, Journal of Computer Sciences Institute 1 (2016) 6–13. DOI: https://doi.org/10.35784/jcsi.60
А. С. Черняев, ETL: обзор инструментов, Молодой ученый, 1 (2019), 23–26, https://moluch.ru/archive/239/55368/, [16.04.2021].
Azure Data Factory documentation, https://docs.microsoft.com/en-us/azure/data-factory/ , [16.04.2021].
R. Sudhir, A. Narain, Understanding Azure Data Factory: Operationalizing Big Data and Advanced Analytics Solutions, Apress, Berkeley, 2019.
A. Leonard, K. Bradshaw, SQL Server Data Automation Through Frameworks. Building Metadata-Driven Frameworks with T-SQL, SSIS, and Azure Data Factory, Apress, Berkeley, 2020. DOI: https://doi.org/10.1007/978-1-4842-6213-9
Dokumentacja narzędzia Azure Synapse Analytics, https://azure.microsoft.com/pl-pl/services/synapse-analytics/, [16.04.2021].
Architektura dedykowanej puli SQL (dawniej SQL DW) w usłudze Azure Synapse Analytics, https://docs.microsoft.com/pl-pl/azure/synapse-analytics/sql-data-warehouse/massively-parallel-processing-mpp-architecture, [16.04.2021].
Wybór między modelami zakupów rdzeń wirtualny i DTU — Azure SQL Database i wystąpienie zarządzane SQL, https://docs.microsoft.com/pl-pl/azure/azure-sql/database/purchasing-models#dtu-based-purchasing-model, [16.04.2021].
Przewodnik dotyczący wydajności i dostrajania przepływu danych, https://docs.microsoft.com/pl-pl/azure/data-factory/concepts-data-flow-performance, [16.04.2021].
Monitorowanie przepływów danych, https://docs.microsoft.com/pl-pl/azure/data-factory/concepts-data-flow-monitoring, [16.04.2021]
Article Details
Abstract views: 1093
License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
