A Real Banking Project on Google Cloud for Data Engineers

While the direct Udemy link is protected from automated scraping, the official course outline and curriculum details reveal exactly what this production-grade project covers.

Created by Shaik Saidhul (SkillVane IT Academy), **“End-to-End GCP Data Engineering Project – Banking Domain”** is a comprehensive, practical course designed to simulate how modern financial institutions process millions of transactions securely and efficiently using Google Cloud Platform (GCP).

### Course Overview & Key Focus

The course focuses heavily on building robust **Batch and Real-Time Data Pipelines** from scratch using an industry-standard banking use case. Instead of working with simple, clean CSV files, you will build a resilient data architecture that mirrors production environments.

### Core Concepts & Architecture Covered

– **Dual Processing Pipelines:** Deep高度 dives into both **Batch processing** (for historical reconciliation) and **Streaming data processing** (for instant transaction tracking).
– **Change Data Capture (CDC):** Capturing database changes instantly to stream transaction details securely.
– **Medallion Architecture:** Structuring your data warehouse into organized layers: **Bronze** (Raw data ingestion), **Silver** (Cleaned and transformed data), and **Gold** (Business-ready aggregates).
– **Data Modeling:** Implementing **Fact and Dimension Modeling** along with **Slowly Changing Dimensions (SCD Type 2)** to maintain a perfect historical record of bank account changes.
– **Metadata-Driven Framework:** Building reusable pipelines controlled by metadata config files rather than hardcoded logic.
– **Production Standards:** Implementing comprehensive logging, automated alerting, exception handling, and real-time fraud detection analytics.

### GCP Tools & Technologies You’ll Learn

– **Dataflow & Apache Beam:** For serverless, scalable stream and batch processing.
– **Dataproc & PySpark:** Running big data transformations using managed Spark clusters.
– **BigQuery:** Setting up an enterprise data warehouse for analytical reporting.
– **Cloud Composer (Apache Airflow):** Orchestrating the entire workflow and scheduling pipelines.
– **CI/CD Pipelines:** Automating updates using **Cloud Build** and **GitHub Actions**.

Author: KYZ

Topics: IT/Programming

Source: https://www.udemy.com/course/end-to-end-gcp-data-engineering-project-banking-domain/