Digitalization of Hindu Temples in India in Google Cloud and SerpAPI Automation in Python

Ramamurthy Valavandan; Subramanian Jagathambal; Gothandapani Balakrishnan; Malarvizhi Balakrishnan; Valavandan Valvandan; Archana Gnanavel; Subramanian Kangalakshmi; Ramamurthy Savitha

doi:doi:10.11648/j.ijics.20220703.12

Digitalization of Hindu Temples in India in Google Cloud and SerpAPI Automation in Python

Ramamurthy Valavandan, Subramanian Jagathambal, Gothandapani Balakrishnan, Malarvizhi Balakrishnan, Valavandan Valvandan, Archana Gnanavel, Subramanian Kangalakshmi, Ramamurthy Savitha

Published in International Journal of Information and Communication Sciences (Volume 7, Issue 3)

Received: 12 July 2022 Accepted: 8 August 2022 Published: 17 August 2022

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Abstract

The study on scraping the Google Search Engine via the SerpAPI and automatically generating a dataset of the temple name, location of the temple, temple description, the state of India, longitude and latitude coordinates, and distance from the major cities in India is demonstrated. The workload orchestration is controlled by Google Composer in Apache Airflow. Google Kubernetes Engine (GKE) categorizes applications into microservices under each container. The docker image is created in the GKE control plane, API scheduler segments the granular levels of microservices in the DevOps operation of GitHub, Jenkins. YAML provides the configuration of the pod, of GKE carrying multiple containers, of the main type of input for Kubernetes configurations. Python application creates the YAML for GKE configuration data to define the Kubernetes object and the business rule. The Google native artificial intelligence VertexAPI picks the keyword of the temple name and the location of every state in India. The SerpAPI application digitized the Hindu temples in India for the complete project life cycle for requirement gathering, design, development, and deployment in Google Cloud. The search parameters are designed for Google Search Engine for the collection of historians, and architectural details of the Hindu temples in India. The Google Search application uses the Google Compute Engine, Big Query, Google Kubernetes Engine, Cloud Composer, and big data services of Data Fusion, Dataproc, and Dataflow. The solution uses Google cloud services of Web Hosting, Load balancing, Google Storage, and the compute engine performance tuning in Big Query optimization. The Google search results are in JSON format of every state in temple details and a python application parses the facts on the Search API algorithm. The temple images are visualized in Python Application and integrated for the data visualization of Google inbuilt Google Studio.

Published in	International Journal of Information and Communication Sciences (Volume 7, Issue 3)
DOI	10.11648/j.ijics.20220703.12
Page(s)	66-81
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), 2024. Published by Science Publishing Group

Keywords

Google Cloud Big Query, Google Data Studio, Python Application Modernization, Data Analytics Engine, SerpAPI Scrape Algorithm, Hindu Temples India, Google Kubernetes Engine, Google Search Dataset

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Ramamurthy Valavandan, Subramanian Jagathambal, Gothandapani Balakrishnan, Malarvizhi Balakrishnan, Valavandan Valvandan, et al. (2022). Digitalization of Hindu Temples in India in Google Cloud and SerpAPI Automation in Python. International Journal of Information and Communication Sciences, 7(3), 66-81. https://doi.org/10.11648/j.ijics.20220703.12

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ACS Style

Ramamurthy Valavandan; Subramanian Jagathambal; Gothandapani Balakrishnan; Malarvizhi Balakrishnan; Valavandan Valvandan, et al. Digitalization of Hindu Temples in India in Google Cloud and SerpAPI Automation in Python. Int. J. Inf. Commun. Sci. 2022, 7(3), 66-81. doi: 10.11648/j.ijics.20220703.12

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AMA Style

Ramamurthy Valavandan, Subramanian Jagathambal, Gothandapani Balakrishnan, Malarvizhi Balakrishnan, Valavandan Valvandan, et al. Digitalization of Hindu Temples in India in Google Cloud and SerpAPI Automation in Python. Int J Inf Commun Sci. 2022;7(3):66-81. doi: 10.11648/j.ijics.20220703.12

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@article{10.11648/j.ijics.20220703.12,
  author = {Ramamurthy Valavandan and Subramanian Jagathambal and Gothandapani Balakrishnan and Malarvizhi Balakrishnan and Valavandan Valvandan and Archana Gnanavel and Subramanian Kangalakshmi and Ramamurthy Savitha},
  title = {Digitalization of Hindu Temples in India in Google Cloud and SerpAPI Automation in Python},
  journal = {International Journal of Information and Communication Sciences},
  volume = {7},
  number = {3},
  pages = {66-81},
  doi = {10.11648/j.ijics.20220703.12},
  url = {https://doi.org/10.11648/j.ijics.20220703.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijics.20220703.12},
  abstract = {The study on scraping the Google Search Engine via the SerpAPI and automatically generating a dataset of the temple name, location of the temple, temple description, the state of India, longitude and latitude coordinates, and distance from the major cities in India is demonstrated. The workload orchestration is controlled by Google Composer in Apache Airflow. Google Kubernetes Engine (GKE) categorizes applications into microservices under each container. The docker image is created in the GKE control plane, API scheduler segments the granular levels of microservices in the DevOps operation of GitHub, Jenkins. YAML provides the configuration of the pod, of GKE carrying multiple containers, of the main type of input for Kubernetes configurations. Python application creates the YAML for GKE configuration data to define the Kubernetes object and the business rule. The Google native artificial intelligence VertexAPI picks the keyword of the temple name and the location of every state in India. The SerpAPI application digitized the Hindu temples in India for the complete project life cycle for requirement gathering, design, development, and deployment in Google Cloud. The search parameters are designed for Google Search Engine for the collection of historians, and architectural details of the Hindu temples in India. The Google Search application uses the Google Compute Engine, Big Query, Google Kubernetes Engine, Cloud Composer, and big data services of Data Fusion, Dataproc, and Dataflow. The solution uses Google cloud services of Web Hosting, Load balancing, Google Storage, and the compute engine performance tuning in Big Query optimization. The Google search results are in JSON format of every state in temple details and a python application parses the facts on the Search API algorithm. The temple images are visualized in Python Application and integrated for the data visualization of Google inbuilt Google Studio.},
 year = {2022}
}

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TY  - JOUR
T1  - Digitalization of Hindu Temples in India in Google Cloud and SerpAPI Automation in Python
AU  - Ramamurthy Valavandan
AU  - Subramanian Jagathambal
AU  - Gothandapani Balakrishnan
AU  - Malarvizhi Balakrishnan
AU  - Valavandan Valvandan
AU  - Archana Gnanavel
AU  - Subramanian Kangalakshmi
AU  - Ramamurthy Savitha
Y1  - 2022/08/17
PY  - 2022
N1  - https://doi.org/10.11648/j.ijics.20220703.12
DO  - 10.11648/j.ijics.20220703.12
T2  - International Journal of Information and Communication Sciences
JF  - International Journal of Information and Communication Sciences
JO  - International Journal of Information and Communication Sciences
SP  - 66
EP  - 81
PB  - Science Publishing Group
SN  - 2575-1719
UR  - https://doi.org/10.11648/j.ijics.20220703.12
AB  - The study on scraping the Google Search Engine via the SerpAPI and automatically generating a dataset of the temple name, location of the temple, temple description, the state of India, longitude and latitude coordinates, and distance from the major cities in India is demonstrated. The workload orchestration is controlled by Google Composer in Apache Airflow. Google Kubernetes Engine (GKE) categorizes applications into microservices under each container. The docker image is created in the GKE control plane, API scheduler segments the granular levels of microservices in the DevOps operation of GitHub, Jenkins. YAML provides the configuration of the pod, of GKE carrying multiple containers, of the main type of input for Kubernetes configurations. Python application creates the YAML for GKE configuration data to define the Kubernetes object and the business rule. The Google native artificial intelligence VertexAPI picks the keyword of the temple name and the location of every state in India. The SerpAPI application digitized the Hindu temples in India for the complete project life cycle for requirement gathering, design, development, and deployment in Google Cloud. The search parameters are designed for Google Search Engine for the collection of historians, and architectural details of the Hindu temples in India. The Google Search application uses the Google Compute Engine, Big Query, Google Kubernetes Engine, Cloud Composer, and big data services of Data Fusion, Dataproc, and Dataflow. The solution uses Google cloud services of Web Hosting, Load balancing, Google Storage, and the compute engine performance tuning in Big Query optimization. The Google search results are in JSON format of every state in temple details and a python application parses the facts on the Search API algorithm. The temple images are visualized in Python Application and integrated for the data visualization of Google inbuilt Google Studio.
VL  - 7
IS  - 3
ER  -

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Author Information

Ramamurthy Valavandan

Nature Labs, Google Projects, Coimbatore, India
Subramanian Jagathambal

Nature Labs, Google Projects, Coimbatore, India
Gothandapani Balakrishnan

Google Engine, Nature Labs, Namakkal, India
Malarvizhi Balakrishnan

Google Engine, Nature Labs, Namakkal, India
Valavandan Valvandan

Nature Labs, Google Projects, Coimbatore, India
Archana Gnanavel

Google Engine, Nature Labs, Namakkal, India
Subramanian Kangalakshmi

Nature Labs, Google Projects, Coimbatore, India
Ramamurthy Savitha

Nature Labs, Google Projects, Coimbatore, India

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Plain Text BibTeX RIS

APA Style

Ramamurthy Valavandan, Subramanian Jagathambal, Gothandapani Balakrishnan, Malarvizhi Balakrishnan, Valavandan Valvandan, et al. (2022). Digitalization of Hindu Temples in India in Google Cloud and SerpAPI Automation in Python. International Journal of Information and Communication Sciences, 7(3), 66-81. https://doi.org/10.11648/j.ijics.20220703.12

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ACS Style

Ramamurthy Valavandan; Subramanian Jagathambal; Gothandapani Balakrishnan; Malarvizhi Balakrishnan; Valavandan Valvandan, et al. Digitalization of Hindu Temples in India in Google Cloud and SerpAPI Automation in Python. Int. J. Inf. Commun. Sci. 2022, 7(3), 66-81. doi: 10.11648/j.ijics.20220703.12

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AMA Style

Ramamurthy Valavandan, Subramanian Jagathambal, Gothandapani Balakrishnan, Malarvizhi Balakrishnan, Valavandan Valvandan, et al. Digitalization of Hindu Temples in India in Google Cloud and SerpAPI Automation in Python. Int J Inf Commun Sci. 2022;7(3):66-81. doi: 10.11648/j.ijics.20220703.12

Copy | Download

@article{10.11648/j.ijics.20220703.12,
  author = {Ramamurthy Valavandan and Subramanian Jagathambal and Gothandapani Balakrishnan and Malarvizhi Balakrishnan and Valavandan Valvandan and Archana Gnanavel and Subramanian Kangalakshmi and Ramamurthy Savitha},
  title = {Digitalization of Hindu Temples in India in Google Cloud and SerpAPI Automation in Python},
  journal = {International Journal of Information and Communication Sciences},
  volume = {7},
  number = {3},
  pages = {66-81},
  doi = {10.11648/j.ijics.20220703.12},
  url = {https://doi.org/10.11648/j.ijics.20220703.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijics.20220703.12},
  abstract = {The study on scraping the Google Search Engine via the SerpAPI and automatically generating a dataset of the temple name, location of the temple, temple description, the state of India, longitude and latitude coordinates, and distance from the major cities in India is demonstrated. The workload orchestration is controlled by Google Composer in Apache Airflow. Google Kubernetes Engine (GKE) categorizes applications into microservices under each container. The docker image is created in the GKE control plane, API scheduler segments the granular levels of microservices in the DevOps operation of GitHub, Jenkins. YAML provides the configuration of the pod, of GKE carrying multiple containers, of the main type of input for Kubernetes configurations. Python application creates the YAML for GKE configuration data to define the Kubernetes object and the business rule. The Google native artificial intelligence VertexAPI picks the keyword of the temple name and the location of every state in India. The SerpAPI application digitized the Hindu temples in India for the complete project life cycle for requirement gathering, design, development, and deployment in Google Cloud. The search parameters are designed for Google Search Engine for the collection of historians, and architectural details of the Hindu temples in India. The Google Search application uses the Google Compute Engine, Big Query, Google Kubernetes Engine, Cloud Composer, and big data services of Data Fusion, Dataproc, and Dataflow. The solution uses Google cloud services of Web Hosting, Load balancing, Google Storage, and the compute engine performance tuning in Big Query optimization. The Google search results are in JSON format of every state in temple details and a python application parses the facts on the Search API algorithm. The temple images are visualized in Python Application and integrated for the data visualization of Google inbuilt Google Studio.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Digitalization of Hindu Temples in India in Google Cloud and SerpAPI Automation in Python
AU  - Ramamurthy Valavandan
AU  - Subramanian Jagathambal
AU  - Gothandapani Balakrishnan
AU  - Malarvizhi Balakrishnan
AU  - Valavandan Valvandan
AU  - Archana Gnanavel
AU  - Subramanian Kangalakshmi
AU  - Ramamurthy Savitha
Y1  - 2022/08/17
PY  - 2022
N1  - https://doi.org/10.11648/j.ijics.20220703.12
DO  - 10.11648/j.ijics.20220703.12
T2  - International Journal of Information and Communication Sciences
JF  - International Journal of Information and Communication Sciences
JO  - International Journal of Information and Communication Sciences
SP  - 66
EP  - 81
PB  - Science Publishing Group
SN  - 2575-1719
UR  - https://doi.org/10.11648/j.ijics.20220703.12
AB  - The study on scraping the Google Search Engine via the SerpAPI and automatically generating a dataset of the temple name, location of the temple, temple description, the state of India, longitude and latitude coordinates, and distance from the major cities in India is demonstrated. The workload orchestration is controlled by Google Composer in Apache Airflow. Google Kubernetes Engine (GKE) categorizes applications into microservices under each container. The docker image is created in the GKE control plane, API scheduler segments the granular levels of microservices in the DevOps operation of GitHub, Jenkins. YAML provides the configuration of the pod, of GKE carrying multiple containers, of the main type of input for Kubernetes configurations. Python application creates the YAML for GKE configuration data to define the Kubernetes object and the business rule. The Google native artificial intelligence VertexAPI picks the keyword of the temple name and the location of every state in India. The SerpAPI application digitized the Hindu temples in India for the complete project life cycle for requirement gathering, design, development, and deployment in Google Cloud. The search parameters are designed for Google Search Engine for the collection of historians, and architectural details of the Hindu temples in India. The Google Search application uses the Google Compute Engine, Big Query, Google Kubernetes Engine, Cloud Composer, and big data services of Data Fusion, Dataproc, and Dataflow. The solution uses Google cloud services of Web Hosting, Load balancing, Google Storage, and the compute engine performance tuning in Big Query optimization. The Google search results are in JSON format of every state in temple details and a python application parses the facts on the Search API algorithm. The temple images are visualized in Python Application and integrated for the data visualization of Google inbuilt Google Studio.
VL  - 7
IS  - 3
ER  -

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