Building Batch Data Pipelines on Google Cloud (edX)

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 will get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
This course is part of the Google Cloud Data Engineer Learning Path Professional Certificate.

What you'll learn

• Review 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

Prerequisites:
To benefit from this course, participants should have completed “Google Cloud Big Data and Machine Learning Fundamentals” or have equivalent experience.
Participant should also have:
• Basic proficiency with a common query language such as SQL.
• Experience with data modeling and ETL (extract, transform, load) activities.
• Experience with developing applications using a common programming language such as Python.
• Familiarity with machine learning and/or statistics

Syllabus

1. Introduction

In this module, we introduce the course and agenda

1. Introduction to Building Batch Data Pipelines

This module reviews different methods of data loading: EL, ELT and ETL and when to use what

1. Executing Spark on Dataproc

This module shows how to run Hadoop on Dataproc, how to leverage Cloud Storage, and how to optimize your Dataproc jobs.

1. Serverless Data Processing with Dataflow

This module covers using Dataflow to build your data processing pipelines.

1. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer

This module shows how to manage data pipelines with Cloud Data Fusion and Cloud Composer.

1. Course Summary

Course Summary

1. Course Resources

PDF links to all modules