A Real Banking Project on Google Cloud for Data Engineers

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.