Building Batch Data Pipelines on Google Cloud is a comprehensive, hands-on course designed to help you master the design, implementation, and optimization of batch data pipelines in the Google Cloud ecosystem. With organizations generating massive volumes of data, the ability to build efficient, reliable, and scalable batch pipelines is essential for modern data engineering and analytics.
 
This course introduces the core concepts of data pipelines, explores common patterns such as EL, ELT, and ETL, and teaches you how to select the right approach based on workload requirements, data volume, transformation complexity, and performance needs. You’ll work directly with Google Cloud’s powerful tools—including BigQuery, Dataproc, Dataflow, Cloud Data Fusion, and Cloud Composer—to build production-ready batch processing solutions.
 
Through guided labs and real-world scenarios, you’ll gain practical experience running Spark on Dataproc, orchestrating end-to-end workflows, leveraging serverless processing with Dataflow, and managing pipelines with Cloud Data Fusion and Cloud Composer.
 
What You’ll Learn
Foundations of batch data pipelines and their role in modern analytics
Understanding pipeline patterns (EL, ELT, ETL) and choosing the right design
Using Google Cloud tools such as BigQuery, Dataproc, Dataflow, and Cloud Data Fusion
Running Apache Spark workloads efficiently on Dataproc
Implementing serverless batch processing with Dataflow
Managing, scheduling, and orchestrating pipelines with Cloud Data Fusion and Cloud Composer
Designing scalable, reliable, and cost-effective batch processing architectures
 
Who This Course Is For
Aspiring and working data engineers
Data analysts transitioning into cloud data engineering
Professionals designing scalable data pipelines for their organization
Learners seeking hands-on experience with Google Cloud’s data processing tools
Anyone wanting to understand batch pipeline patterns and best practices