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A Real Banking Project on Google Cloud for Data Engineers
Published 7/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 7h 5m | Size: 4.4 GB
Build Cloud SQL, GCS, Pub/Sub, BigQuery, Dataproc, PySpark and Airflow pipelines in a real banking project
What you'll 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.
Requirements
Basic understanding of SQL, Python is helpful.
Basic familiarity with Google Cloud Platform is useful, but the required setup is explained in the course.
No prior real-world data engineering project experience is required. The course is designed to help learners build a complete project step by step.
Learners should have a Google Cloud account to practice the project hands-on.
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.
Who this course is for
Aspiring data engineers who want hands-on project experience with Google Cloud Platform.
Learners preparing for data engineering interviews who need a practical banking project to explain confidently.
Students and freshers who already know basic SQL or Python and want to move into data engineering.
Cloud engineers, data analysts, or ETL developers who want to transition into GCP data engineering roles.
Working professionals who want to learn how batch and streaming pipelines are built on GCP.
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