Authors :
Pooja D. Kavishwar; Dr. S. R. Pande
Volume/Issue :
Volume 8 - 2023, Issue 7 - July
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/yenr4x5s
DOI :
https://doi.org/10.5281/zenodo.8186386
Abstract :
This research paper aims to investigate the
process of creating and loading a data warehouse model
on cloud platforms and evaluate its performance. With
the increasing adoption of cloud computing,
organizations are leveraging cloud platforms to store
and process large volumes of data for analytics purposes.
By examining the data warehouse model creation,
loading procedures, and performance metrics on
different cloud platforms, this study aims to provide
insights into the strengths and weaknesses of various
platforms in supporting efficient and scalable data
warehousing solutions.
Keywords :
Data Warehouse models; Conceptual Data Models; Physical Data Models; Analytics Query; Query Response Time.
This research paper aims to investigate the
process of creating and loading a data warehouse model
on cloud platforms and evaluate its performance. With
the increasing adoption of cloud computing,
organizations are leveraging cloud platforms to store
and process large volumes of data for analytics purposes.
By examining the data warehouse model creation,
loading procedures, and performance metrics on
different cloud platforms, this study aims to provide
insights into the strengths and weaknesses of various
platforms in supporting efficient and scalable data
warehousing solutions.
Keywords :
Data Warehouse models; Conceptual Data Models; Physical Data Models; Analytics Query; Query Response Time.
