This comprehensive course takes you from the very basics of GCP to advanced data engineering and analytics workflows. You’ll start by understanding what data engineering is on Google Cloud Platform, what prerequisites you need, and how to set up your environment correctly. We’ll show you how to configure VS Code workspaces, create Python virtual environments, and prepare your system for development so you can learn by doing.

You’ll then get hands-on with Google Cloud Platform itself, from creating and managing your GCP account to working with Google Cloud Shell, the SDK, and the gcloud CLI. We’ll cover free tier credits, project management, and give you a guided tour of the analytics services available on GCP. Next, you’ll learn how to build data lakes using Google Cloud Storage (GCS), upload and manage files via the web UI, gsutil, and Python libraries, design bucket architectures, handle blob storage programmatically, and convert data to Parquet or JSON for downstream processing.

As the course progresses, you’ll set up Postgres databases with Cloud SQL, configure secure access and IAM roles, and integrate your databases with Python applications using Pandas for ETL and data transformations. You’ll also learn to store credentials securely with Secret Manager and automate secure database connections. For big data workloads, you’ll deploy Dataproc clusters, configure development environments, and run PySpark, Scala, and Spark SQL jobs on HDFS and GCS datasets. The course covers ELT pipeline design with Dataproc workflow templates, gcloud CLI automation, and cost-optimised cluster management.

Finally, you’ll expand your skills to Databricks on GCP, learning to create workspaces, manage DBFS storage, process data with Spark SQL and PySpark, handle JSON schemas, and orchestrate complex ELT pipelines with Databricks Jobs and Workflows. This combination of tools gives you a deep understanding of modern cloud-based data engineering, preparing you for real-world projects and certifications.