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A Real Banking Project on Google Cloud for Data Engineers (Udemy.com)

Build Cloud SQL, GCS, Pub/Sub, BigQuery, Dataproc, PySpark and Airflow pipelines in a real banking project

4.6

26 reviews

Created by: Saidhul Shaik

Last updated July 2026

What you will learn

Build an end-to-end banking data engineering project on Google Cloud Platform.
Ingest batch data from Cloud SQL to Google Cloud Storage using production-style pipeline design.
Build a streaming ingestion pipeline from Pub/Sub to BigQuery for real-time banking events.
Create Bronze, Silver, and Gold data layers using PySpark on a Dataproc cluster.
Orchestrate banking data pipelines with Apache Airflow DAGs.
Set up CI/CD for Airflow workflows using GitHub and Cloud Build.
Understand how Cloud SQL, GCS, Pub/Sub, BigQuery, Dataproc, PySpark, and Airflow work together in a real project.
Design a portfolio-ready GCP data platform architecture for a banking use case.

How it compares

Against the other 1,182 rated Udemy courses in IT & Software on CourseDuck:

Rating4.6 out of 5higher than 67% of them
Reviews26#1,167 of 1,183 by number of reviews
Length7.1 hourslonger than 59% of them
Last updatedJuly 2026more recently than 81% of them

Course Description

This course is a hands-on GCP data engineering project built around a realistic banking data platform. Instead of learning services separately, you will see how multiple Google Cloud services work together to solve a complete data engineering use case from source ingestion to curated analytics layers.

You will start with the project architecture and banking dataset, then set up the required GCP resources including Cloud SQL, Pub/Sub, Google Cloud Storage, BigQuery and Dataproc. From there, you will build a batch ingestion pipeline from Cloud SQL to GCS and a streaming ingestion pipeline from Pub/Sub to BigQuery.

The course then moves into medallion-style data processing. You will create Bronze, Silver and Gold layers, load data with PySpark on Dataproc, and prepare business-ready tables that can be used for analytics and reporting. Finally, you will orchestrate the full banking pipeline using Apache Airflow DAGs and learn how CI/CD can be connected with GitHub and Cloud Build.

By the end of the course, you will have a practical, portfolio-ready GCP banking project that you can explain in interviews. This course is especially useful for aspiring data engineers, cloud engineers, ETL developers, data analysts and learners who want real project experience with Google Cloud data engineering tools.

All Levels

Time: 7.1h

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Instructor Details

Saidhul Shaik

4.6 Rating
26 Reviews

I am a seasoned Cloud Data Engineer with expertise in GCP, Azure, and AWS, specializing in data engineering, analytics, and DevOps. With years of hands-on experience in building scalable data pipelines, optimizing Apache Spark, and managing cloud migrations, I am passionate about helping learners bridge the gap between theory and real-world applications. As the founder of Skill Vane Software Institute, I mentor aspiring data engineers, guiding them through industry-standard best practices, hands-on projects, and interview preparation. My goal is to empower professionals with the skills needed to thrive in cloud and big data ecosystems.