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

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
Google Cloud Big Data and Machine Learning Fundamentals (edX) EdX
Google Cloud

Google Cloud Big Data and Machine Learning Fundamentals (edX)

Computer Science

Data Analysts, Data Engineers, Data Scientists, and ML Engineers who are getting started with Google Cloud. This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

Self Paced
Self-Paced
Machine Learning Big Data BigQuery
Details
Data Processing Using Python (Coursera) Coursera
Nanjing University

Data Processing Using Python (Coursera)

CS: Software Engineering Statistics & Data Analysis

This course is mainly for non-computer majors. It starts with the basic syntax of Python, to how to acquire data in Python locally and from network, to how to present data, then to how to conduct basic and advanced statistic analysis and visualization of data, and finally to how to design a simple GUI to present and process data, advancing level by level.

Nov 23rd 2026
5-12 Weeks
Python Data Structures Data Analysis
Details
Distributed Machine Learning with Apache Spark (edX) EdX
University of California, Berkeley,BerkeleyX

Distributed Machine Learning with Apache Spark (edX)

CS: Software Engineering Statistics & Data Analysis

Learn the underlying principles required to develop scalable machine learning pipelines and gain hands-on experience using Apache Spark. Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability and optimization.

No sessions available
4 Weeks
Algorithms Machine Learning Exploratory Data Analysis
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
Smart Analytics, Machine Learning, and AI on Google Cloud (edX) EdX
Google Cloud

Smart Analytics, Machine Learning, and AI on Google Cloud (edX)

Computer Science

This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customization required. Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customization required.

Self Paced
Self-Paced
ML Artificial Intelligence SQL
Details
Big Data Analytics Using Spark (edX) EdX
University of California, San Diego,UC San DiegoX

Big Data Analytics Using Spark (edX)

Statistics & Data Analysis Data Science

Learn how to analyze large datasets using Jupyter notebooks, MapReduce and Spark as a platform. In data science, data is called “big” if it cannot fit into the memory of a single standard laptop or workstation. The analysis of big datasets requires using a cluster of tens, hundreds or thousands of computers. Effectively using such clusters requires the use of distributed files systems, such as the Hadoop Distributed File System (HDFS) and corresponding computational models, such as Hadoop, MapReduce and Spark.

Dec 5th 2023
5-12 Weeks
Machine Learning Big Data Hadoop
Details
Authoritative GCP (edX) EdX
AI (Pragmatic AI Labs)

Authoritative GCP (edX)

Computer Science

Master Google Cloud Architecture and prepare for the Professional Cloud Architect certification exam through hands-on labs and expert instruction. This comprehensive course, designed for cloud architects and developers, covers the essential skills needed to design, plan, and manage robust enterprise solutions on Google Cloud Platform (GCP).

Self Paced
Self-Paced
GCP Google Cloud Platform Google Cloud
Details
Getting Started with Terraform for Google Cloud (edX) EdX
Google Cloud

Getting Started with Terraform for Google Cloud (edX)

Computer Science

DevOps engineers, Cloud architects, and Cloud engineers who want to start using Terraform to automate infrastructure provisioning with a focus on Google Cloud Platform. This course provides an introduction to using Terraform for Google Cloud. It enables learners to describe how Terraform can be used to implement infrastructure as code and to apply some of its key features and functionalities to create and manage Google Cloud infrastructure.

Self Paced
Self-Paced
Google Cloud Terraform Google Cloud Engineer Learning Path Professional Certificate
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