GCP Data Engineering

Learn Google Cloud Platform (GCP) Data Engineering from a real-world industry perspective, designed for aspiring data engineers, software engineers, and analytics professionals looking to build scalable data solutions.

This course covers end-to-end data engineering workflows on GCP, including data ingestion, processing, storage, and analytics using industry-standard tools.

What you will learn:

• Build batch and real-time data pipelines using Cloud Dataflow (Apache Beam)
• Work with BigQuery for large-scale data warehousing and analytics
• Design data lakes using Google Cloud Storage
• Orchestrate workflows using Cloud Composer (Airflow)
• Stream data using Pub/Sub
• Understand data modeling, partitioning, clustering, and performance optimization
• Implement best practices used in real-world production systems

Why this course?

• Taught by a Staff Data Engineer with experience in large-scale systems.
• Focus on hands-on learning + real-world scenarios
• Guidance on interview preparation and system design for data engineering roles
• Covers industry use cases, not just theory

Who should join:

• Software Engineers transitioning to Data Engineering
• Data Analysts looking to scale into big data roles
• Freshers aiming for cloud/data careers
• Professionals preparing for GCP Data Engineer roles/interviews

Prerequisites:

• Basic knowledge of SQL
• Familiarity with Python or Java (recommended but not mandatory)