scholarly journals The comparative analysis of modern ETL tools

2021 ◽  
Vol 19 ◽  
pp. 126-131
Author(s):  
Vitalii Mayuk ◽  
Ivan Falchuk ◽  
Piotr Muryjas
Keyword(s):  
Comparative Analysis ◽  
Data Warehouse ◽  
Execution Time ◽  
Analytical Data ◽  
Time Efficiency ◽  
Transactional Data ◽  
Research Efficiency

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.

Recommendations for deploying multidimensio

2017 ◽  
Vol 11 (1) ◽  
pp. 68-80
Author(s):  
E. E. Akimkina
Keyword(s):  
Comparative Analysis ◽  
Management System ◽  
Analytical Data ◽  
Enterprise Management ◽  
Multidimensional Data ◽  
Dimensional Approach ◽  
Process Data ◽  
Modeling Systems ◽  
Enterprise Management System ◽  
Practical Recommendations

A comparative analysis of different approaches to analytical data and shows that the most ample opportunities has a multi-dimensional approach, implemented with the help of OLAP technology. Presented multidimensional OLAP-cube model with the measurements for the analysis and processing of process data. Practical recommendations for the deployment of a multidimensional data modeling systems with regard to their integration into existing enterprise management system.

scholarly journals Data Analytical Processing in Data Warehouses

2010 ◽  
Vol 2 (1) ◽  
pp. 99-116
Author(s):  
Katarzyna Rostek
Keyword(s):  
Data Processing ◽  
Data Warehouse ◽  
Analytical Data ◽  
Data Warehouses ◽  
Practical Guidelines ◽  
Processing Information ◽  
Scope Of Application ◽  
Analytical Processing ◽  
Effective Use

Data Analytical Processing in Data Warehouses The article presents issues connected with processing information from data warehouses (the analytical enterprise databases) and two basic types of analytical data processing in data warehouse. The genesis, main definitions, scope of application and real examples from business implementations will be described for each type of analysis. There will be presented copyrighted method of knowledge discovering in databases, together with practical guidelines for its proper and effective use in the enterprise.

A Comparative Analysis of Traditional and Cloud Data Warehouse

2018 ◽  
Vol 15 (1) ◽  
pp. 34
Author(s):  
Khawaja Ubaid ur Rehman ◽  
Umair Ahmad ◽  
Sajid Mahmood
Keyword(s):  
Comparative Analysis ◽  
Data Warehouse ◽  
Cloud Data

Data Warehouse Maintenance, Evolution and Versioning

2010 ◽  
pp. 171-188 ◽  
Author(s):  
Johann Eder ◽  
Karl Wiggisser
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Building Blocks ◽  
Data Warehouses ◽  
Master Data ◽  
Analytical Processing ◽  
The Common ◽  
Instance Analysis ◽  
Transactional Data ◽  
Common Understanding

Data Warehouses typically are building blocks of decision support systems in companies and public administration. The data contained in a data warehouse is analyzed by means of OnLine Analytical Processing tools, which provide sophisticated features for aggregating and comparing data. Decision support applications depend on the reliability and accuracy of the contained data. Typically, a data warehouse does not only comprise the current snapshot data but also historical data to enable, for instance, analysis over several years. And, as we live in a changing world, one criterion for the reliability and accuracy of the results of such long period queries is their comparability. Whereas data warehouse systems are well prepared for changes in the transactional data, they are, surprisingly, not able to deal with changes in the master data. Nonetheless, such changes do frequently occur. The crucial point for supporting changes is, first of all, being aware of their existence. Second, once you know that a change took place, it is important to know which change (i.e., knowing about differences between versions and relations between the elements of different versions). For data warehouses this means that changes are identified and represented, validity of data and structures are recorded and this knowledge is used for computing correct results for OLAP queries. This chapter is intended to motivate the need for powerful maintenance mechanisms for data warehouse cubes. It presents some basic terms and definitions for the common understanding and introduces the different aspects of data warehouse maintenance. Furthermore, several approaches addressing the problem are presented and classified by their capabilities.

A comparative analysis of data warehouse data models

2016 ◽  
Author(s):  
Ivan Bojicic ◽  
Zoran Marjanovic ◽  
Nina Turajlic ◽  
Marko Petrovic ◽  
Milica Vuckovic ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Data Warehouse ◽  
Data Models

Complementing the Data Warehouse with Information Filtered from the Web

2011 ◽  
pp. 206-218
Author(s):  
Witold Abramowicz ◽  
Pawel Jan Kalczynski ◽  
Krzysztof Wecel
Keyword(s):  
Data Warehouse ◽  
Claim Data ◽  
Textual Information ◽  
User Models ◽  
Transactional Data ◽  
The Web

The data warehouse is considered to be the best way to organize transactional data. However, as many researches claim data warehouse should be augmented with external textual information. The objective of this chapter is to examine the requirements for profiling in the data warehouse environment. Profiles created in the data warehouse are then utilized to filter information. The goal of the sketched system is to support users in his situated actions. We explore many issues concerning personalization, such as information overflow, user models, and situatedness. We also analyze the factors that influence the filtering process. Finally, we draw some conclusions that should be considered during extension of the evaluated system.

Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation

2021 ◽  
pp. 337-363
Author(s):  
Nidia Rodríguez-Mazahua ◽  
Lisbeth Rodríguez-Mazahua ◽  
Asdrúbal López-Chau ◽  
Giner Alor-Hernández ◽  
S. Gustavo Peláez-Camarena
Keyword(s):  
Comparative Analysis ◽  
Decision Tree ◽  
Data Warehouse ◽  
Tree Algorithms

A Novel Approach Using Non-Synonymous Materialized Queries for Data Warehousing

2021 ◽  
Vol 17 (3) ◽  
pp. 22-43
Author(s):  
Sonali Ashish Chakraborty
Keyword(s):  
Data Warehouse ◽  
Relational Database ◽  
Execution Time ◽  
Data Warehousing ◽  
Multiple Sources ◽  
Novel Approach ◽  
Union Operation ◽  
Data Marts ◽  
Using Data

Data from multiple sources are loaded into the organization data warehouse for analysis. Since some OLAP queries are quite frequently fired on the warehouse data, their execution time is reduced by storing the queries and results in a relational database, referred as materialized query database (MQDB). If the tables, fields, functions, and criteria of input query and stored query are the same but the query criteria specified in WHERE or HAVING clause do not match, then they are considered non-synonymous to each other. In the present research, the results of non-synonymous queries are generated by reusing the existing stored results after applying UNION or MINUS operations on them. This will reduce the execution time of non-synonymous queries. For superset criteria values of input query, UNION operation is applied, and for subset values, MINUS operation is applied. Incremental result processing of existing stored results, if required, is performed using Data Marts.

High Performance Analytical Data Reconstructing Strategy in Data Warehouse

2005 ◽  
pp. 527-531
Author(s):  
Xiufeng Xia ◽  
Qing Li ◽  
Lingyu Xu ◽  
Weidong Sun ◽  
Suixiang Shi ◽  
...  
Keyword(s):  
Data Warehouse ◽  
High Performance ◽  
Analytical Data

scholarly journals Multicriteria assessment of selected types of ceilings

2018 ◽  
Vol 219 ◽  
pp. 04011
Author(s):  
Julia Nowak ◽  
Edyta Plebankiewicz
Keyword(s):  
Comparative Analysis ◽  
Thermal Insulation ◽  
Execution Time ◽  
Lower Surface ◽  
Sound Insulation ◽  
Multicriteria Assessment ◽  
Comparison Criteria

The paper compares three systems of the rib-and-slab ceiling: Vector, Rector and Teriva. The following comparison criteria were selected: the tare weight of ceiling beams, execution time, slab faulting, sound insulation, thermal insulation and aesthetics of the lower surface. The results of the multicriteria comparative analysis clearly indicated the advantage of the Vector ceiling, assuming the same weights for all criteria taken into account.

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