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Article Open Access December 21, 2021

Optimizing Data Warehousing for Large Scale Policy Management Using Advanced ETL Frameworks

Avinash Reddy Aitha 1,*
1
Lead SDET, USA
Publihed in: Current Research in Public Health (Volume 1, Issue 1, 2021)
Page(s): 1-19
DOI: 10.31586/jaibd.2021.1350
Received
August 26, 2021
Revised
November 29, 2021
Accepted
December 19, 2021
Published
December 21, 2021
Keywords
Data Warehousing; Data Collection; Data Management; Data Presentation; Historical Data; Large-Scale Policy Management; ETL Framework; Advanced ETL Techniques; Performance Optimization; Reporting; Analysis; Data Marts; Mission-Critical Systems; Petabytes; Data Cleansing; Data Transformation; Data Integration; Real-Time Reporting; Incremental Loading; Parallel Mechanisms
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2021. Published by Scientific Publications
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Cite This Article

APA Style
Aitha, A. R. (2021). Optimizing Data Warehousing for Large Scale Policy Management Using Advanced ETL Frameworks. Current Research in Public Health, 1(1), 1-19. https://doi.org/10.31586/jaibd.2021.1350
ACS Style
Aitha, A. R. Optimizing Data Warehousing for Large Scale Policy Management Using Advanced ETL Frameworks. Current Research in Public Health 2021 1(1), 1-19. https://doi.org/10.31586/jaibd.2021.1350
Chicago/Turabian Style
Aitha, Avinash Reddy. 2021. "Optimizing Data Warehousing for Large Scale Policy Management Using Advanced ETL Frameworks". Current Research in Public Health 1, no. 1: 1-19. https://doi.org/10.31586/jaibd.2021.1350
AMA Style
Aitha AR. Optimizing Data Warehousing for Large Scale Policy Management Using Advanced ETL Frameworks. Current Research in Public Health. 2021; 1(1):1-19. https://doi.org/10.31586/jaibd.2021.1350 
@Article{crph1350,
AUTHOR = {Aitha, Avinash Reddy},
TITLE = {Optimizing Data Warehousing for Large Scale Policy Management Using Advanced ETL Frameworks},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2021},
NUMBER = {1},
PAGES = {1-19},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1350},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2021.1350},
ABSTRACT = {Data warehousing is a technique for collecting, managing, and presenting data to help people analyze and use that data effectively. It involves a large database designed to support management-level staff by providing all the relevant historical data for analysis. This chapter begins with a definition of data warehousing, followed by an overview of large-scale policy management to highlight the need for data warehousing. Next, an overview of an ETL framework is presented, along with a discussion of advanced ETL techniques. The chapter concludes with an outline of performance optimization techniques for data warehousing. Data warehousing is considered a key enabler for efficient reporting and analysis, with implementation choices ranging from cost-effective desktop systems to large-scale, mission-critical data marts and warehouses containing petabytes of data. Extract, transform, and load (ETL) systems remain one of the largest cost and effort areas within data warehouse development projects, requiring significant planning and resources to build, manage, and monitor the flow of data from source systems into the data warehouse. The technology and techniques used for ETL can greatly influence the success or failure of a data warehouse. Complex business requirements for data cleansing, loading, transformation, and integration have intensified, while operational plans for real-time and near-real-time reporting add additional challenges. Parallel loading mechanisms, incremental data loading, and runtime update and insert strategies not only improve ETL performance but also optimize data warehousing performance, particularly for large-scale policy management.},
}
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AB  - Data warehousing is a technique for collecting, managing, and presenting data to help people analyze and use that data effectively. It involves a large database designed to support management-level staff by providing all the relevant historical data for analysis. This chapter begins with a definition of data warehousing, followed by an overview of large-scale policy management to highlight the need for data warehousing. Next, an overview of an ETL framework is presented, along with a discussion of advanced ETL techniques. The chapter concludes with an outline of performance optimization techniques for data warehousing. Data warehousing is considered a key enabler for efficient reporting and analysis, with implementation choices ranging from cost-effective desktop systems to large-scale, mission-critical data marts and warehouses containing petabytes of data. Extract, transform, and load (ETL) systems remain one of the largest cost and effort areas within data warehouse development projects, requiring significant planning and resources to build, manage, and monitor the flow of data from source systems into the data warehouse. The technology and techniques used for ETL can greatly influence the success or failure of a data warehouse. Complex business requirements for data cleansing, loading, transformation, and integration have intensified, while operational plans for real-time and near-real-time reporting add additional challenges. Parallel loading mechanisms, incremental data loading, and runtime update and insert strategies not only improve ETL performance but also optimize data warehousing performance, particularly for large-scale policy management.
DO  - Optimizing Data Warehousing for Large Scale Policy Management Using Advanced ETL Frameworks
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