Udemy - GCP Data Engineering - End to End Project - Retailer Domain

Industry Standard Project in Retailer Domain using GCP services like GCS, BigQuery, Dataproc, Composer, GitHub, CICD
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
Requirements
Basic Knowledge on Python and SQL
Description
This project focuses on building a data lake in Google Cloud Platform (GCP) for Retailer DomainThe 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 ApproachSCD type 2 implementationCDM(Common Data Model)Medallion Architecture Logging and MonitoringError HandlingOptimizationsCICD implementationmany more best practicesData SourcesEMR (Electronic Medical Records) data from two hospitalsClaims filesCPT (Current Procedural Terminology) CodeNPI (National Provider Identifier) DataExpected OutcomesEfficient 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.
Who this course is for
Aspiring Data Engineers, Data Professionals
For getting interview Ready
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