EdX

Building Batch Data Pipelines on Google Cloud (edX)

Offered by Google Cloud,
Building Batch Data Pipelines on Google Cloud (edX)

Developers responsible for designing pipelines and architectures for data processing. Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. This course describes which paradigm should be used and when for batch data.

Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.

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

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

ML Pipelines on Google Cloud (Coursera) Coursera
Google Cloud

ML Pipelines on Google Cloud (Coursera)

Data Science

In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata.

Nov 9th 2026
4 Weeks
ML Machine Learning Google Cloud
Details
Big Data Analysis with Scala and Spark (Coursera) Coursera
École Polytechnique Fédérale de Lausanne

Big Data Analysis with Scala and Spark (Coursera)

CS: Theory CS: Programming

Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout.

Nov 9th 2026
4 Weeks
Programming Algorithms SQL
Details
Getting Started with Google Kubernetes Engine (edX) EdX
Google Cloud

Getting Started with Google Kubernetes Engine (edX)

Computer Science

This class is intended for the following participants: - Application developers, Cloud Solutions Architects, DevOps Engineers, IT managers. - Individuals using Google Cloud to create new solutions or to integrate existing systems, application environments, and infrastructure with Google Cloud. - Executives and business decision makers evaluating the potential of GKE to address business needs.

Self Paced
Self-Paced
Kubernetes Google Kubernetes Engine Google Cloud
Details
Introduction to Apache Spark (edX) EdX
University of California, Berkeley

Introduction to Apache Spark (edX)

CS: Software Engineering Statistics & Data Analysis

Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals. Spark is rapidly becoming the compute engine of choice for big data. Spark programs are more concise and often run 10-100 times faster than Hadoop MapReduce jobs. As companies realize this, Spark developers are becoming increasingly valued.

Not Available
Course Not Available
Programming Big Data Apache Spark
Details
Understanding Your Google Cloud Costs en Español (Coursera) Coursera
Google Cloud

Understanding Your Google Cloud Costs en Español (Coursera)

CS: Information & Technology

Understanding Your Google Cloud Platform (GCP) Costs es más apropiado para quienes desempeñan cargos relacionados con la tecnología o las finanzas, y son responsables de administrar los costos de GCP. Aprenderá a configurar una cuenta de facturación, organizar recursos y administrar permisos de acceso a la facturación.

Nov 9th 2026
3 Weeks
GCP Google Sheets Google Cloud Platform
Details
Machine Learning Operations 2 (MLOps2-AWS): Data Pipeline Automation & Optimization using Amazon Web Services (AWS) (edX) EdX
Statistics.comX,Statistics.com

Machine Learning Operations 2 (MLOps2-AWS): Data Pipeline Automation & Optimization using Amazon Web Services (AWS) (edX)

Statistics & Data Analysis Data Science

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course - MLOp2s: Data Pipeline Automation & Optimization using Amazon Web Services (AWS).

Self Paced
Self-Paced
Optimization Automation Continuous Integration
Details
Hacking PostgreSQL: Data Access Methods (edX) EdX
Ural Federal University,UrFUx

Hacking PostgreSQL: Data Access Methods (edX)

CS: Software Engineering Statistics & Data Analysis

Learn the science, engineering practices and hacking techniques of data access – core aspects of information processing in a database. This course is about data storage and data processing technologies with examples from PostgreSQL. It is geared toward database core developers, operation systems developers, system architects, and all those who want to understand databases in more detail.

No sessions available
13-24 Weeks
Algorithms Hacking Data Access
Details