Course Overview
TOPData pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
Scheduled Classes
TOP| 06/02/25 - TDV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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| 11/17/25 - TDV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOPReview different methods of data loading: EL, ELT and ETL and when to use what
- Run Hadoop on Dataproc, leverage Cloud Storage, and optimize Dataproc jobs
- Build your data processing pipelines using Dataflow
- Manage data pipelines with Data Fusion and Cloud Composer
Outline
TOP
Viewing outline for:
In this module, we introduce the course and agenda
- This module reviews different methods of data loading: EL, ELT and ETL and when to use what
- This module shows how to run Hadoop on Dataproc, how to leverage Cloud Storage, and how to optimize your Dataproc jobs.
- This module covers using Dataflow to build your data processing pipelines
- This module shows how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
- Course Summary
- PDF links to all modules
Prerequisites
TOPExperience with data modeling and ETL (extract, transform, load) activities.
- Experience with developing applications by using a common programming language such as Python or Java.
Who Should Attend
TOPDevelopers responsible for designing pipelines and architectures for data processing.
