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

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.