| File Name: | GCP Data Engineering – End to End Project – Retailer Domain |
| Content Source: | https://www.udemy.com/course/gcp-data-engineering-end-to-end-project-retailer-domain/ |
| Genre / Category: | Other Tutorials |
| File Size : | 2.6 GB |
| Publisher: | udemy |
| Updated and Published: | May 27, 2025 |
What you’ll learn
- Understand the End to End Data Engineering Project for Retailer Domain
- Design and Implement Scalable ETL Pipelines for Healthcare Data
- Implement Key Techniques like Incremental Data, SCD2, Metadata driven approach, Medallion Arch, Error Handling, CDM , CICD & Many more..
- Develop and Deploy Data Solutions with CI/CD Practices
- This project focuses on building a data lake in Google Cloud Platform (GCP) for Retailer Domain
- The goal is to centralize, clean, and transform data from multiple sources, enabling Retailers providers and insurance companies to streamline billing, claims processing, and revenue tracking.
- GCP Services Used:
- Google Cloud Storage (GCS): Stores raw and processed data files.
- BigQuery: Serves as the analytical engine for storing and querying structured data.
- Dataproc: Used for large-scale data processing with Apache Spark.
- Cloud Composer (Apache Airflow): Automates ETL pipelines and workflow orchestration.
- Cloud SQL (MySQL): Stores transactional Electronic Medical Records (EMR) data.
- GitHub & Cloud Build: Enables version control and CI/CD implementation.
- CICD (Continuous Integration & Continuous Deployment): Automates deployment pipelines for data processing and ETL workflows.
- Techniques involved :
- Metadata Driven Approach
- SCD type 2 implementation
- CDM(Common Data Model)
- Medallion Architecture
- Logging and Monitoring
- Error Handling
- Optimizations
- CICD implementation
- many more best practices
- Data Sources
- EMR (Electronic Medical Records) data from two hospitals
- Claims files
- CPT (Current Procedural Terminology) Code
- NPI (National Provider Identifier) Data
- Expected Outcomes
- Efficient Data Pipeline: Automating the ingestion and transformation of RCM data.
- Structured Data Warehouse: gold tables in BigQuery for analytical queries.
- KPI Dashboards: Insights into revenue collection, claims processing efficiency, and financial trends.
DOWNLOAD LINK: GCP Data Engineering – End to End Project – Retailer Domain
GCP_Data_Engineering_End_to_End_Project_Retailer_Domain.part1.rar – 1.5 GB
GCP_Data_Engineering_End_to_End_Project_Retailer_Domain.part2.rar – 1.1 GB
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