Bank_Position_Process

Automated Bank Loan and Investment Status Pipeline — Google Cloud Platform

This repository contains a simplified, demonstration version of a real-world GCP project. It illustrates the structure, configurations, and code required to automate a bank loan and investment status process using Google Cloud Platform services.

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Repository Structure

File/Folder	Purpose
scripts/	Python scripts containing SQL queries for extracting data and loading it into BigQuery target tables. Note: The production environment contains 12+ scripts. This demo includes only a subset for clarity.
accp.yaml	Pipeline configuration file defining build instructions for the container image, including image name, build context, and other ACCP pipeline parameters. Referenced during pipeline initialization.
Dockerfile	Image build specification defining the base image, system dependencies, and Python packages required to execute the scripts. Referenced in accp.yaml.
requirements.txt	List of Python dependencies to be installed in the image environment. Referenced in the Dockerfile.
positions_dag.py	Apache Airflow DAG for orchestrating and scheduling the end-to-end process. References the container image built from the above files and defines execution logic and scheduling parameters.

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Workflow Overview

1. Code and Configuration — Python scripts, configuration files, and dependencies are stored in this repository.
2. Image Build — The ACCP pipeline uses accp.yaml, Dockerfile and requirement.txt to build a container image with all required dependencies.
3. Data Processing — The container runs Python scripts to fetch, transform, and load data into BigQuery.
4. Orchestration — Airflow triggers the container execution according to the schedule defined in positions_dag.py.

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