Master Google Dataflow with hands-on projects | Apache Beam basics to advanced streaming & batch data pipelines
Are you looking to master Google Dataflow and Apache Beam to build scalable, production-ready data pipelines on Google Cloud Platform (GCP)? Whether you’re a data engineer, cloud enthusiast, or aspiring GCP professional, this course will take you from zero to advanced level, through hands-on labs, real-world case studies, and practical assignments.
What you’ll learn
- Understand what Google Cloud Dataflow is and how it enables scalable data processing.
- Learn the Apache Beam programming model, with PCollections and PTransforms.
- Build end-to-end ETL pipelines for both batch and streaming data.
- Use Google Pub/Sub for real-time data ingestion and understand its architecture.
- Implement template-based pipelines for reusability and automation.
Course Content
- Dataflow with Apache Beam –> 9 lectures • 5hr 36min.
Requirements
Are you looking to master Google Dataflow and Apache Beam to build scalable, production-ready data pipelines on Google Cloud Platform (GCP)? Whether you’re a data engineer, cloud enthusiast, or aspiring GCP professional, this course will take you from zero to advanced level, through hands-on labs, real-world case studies, and practical assignments.
What You’ll Learn
- Understand the fundamentals of Google Cloud Dataflow and how it fits in the data engineering ecosystem
- Explore the Apache Beam framework – the programming model behind Dataflow
- Learn core concepts like PCollections and PTransforms
- Differentiate Dataflow vs Dataproc and when to use each
- Set up your own Cloud Workbench environment for hands-on practice
- Build real-world ETL pipelines (Extract, Transform, Load) using Apache Beam
- Use Google Pub/Sub for real-time data ingestion and understand its architecture
- Develop pipelines using both:
- Template-based method
- Case Study 1: Template-driven pipeline
- Custom code approach
- Case Study 2: end to end Batch pipeline
- Case Study 3: end to end Streaming pipeline
- Template-based method
- Complete hands-on assignments to reinforce learning and prepare for real-world scenarios
Hands-On Labs Include:
- Beam Basics with Python/Java SDK
- ETL development on Dataflow
- Streaming pipeline using Pub/Sub
- Batch pipeline using Cloud Storage
- Debugging, monitoring, and optimizing pipeline performance
- end to end pipeline creations from scratch