In this course, you learn about data engineering on Google Cloud, the roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud. You also learn about ways to address data engineering challenges.
Introduction to Data Engineering on Google Cloud
Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Understand the role of a data engineer.
Identify data engineering tasks and core components used on Google Cloud.
Understand how to create and deploy data pipelines of varying patterns on Google Cloud.
Identify and utilize various automation techniques on Google Cloud.
Details to know
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Recently updated!
September 2024
Assessments
6 assignments
Taught in English
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There are 8 modules in this course
This section welcomes you to the Introduction to Data Engineering on Google Cloud course, and provides an overview of the course structure and goals.
What's included
1 video
This module provides an introduction to the role of a data engineer. It covers key concepts such as data sources and sinks, data formats, storage options on Google Cloud, metadata management, and the use of Analytics Hub for data sharing within and outside an organization.
What's included
9 videos1 assignment1 app item
This module provides an overview of data replication and migration on Google Cloud. It covers the basic architecture, the 'gcloud' command-line tool, Storage Transfer Service, Transfer Appliance, and Datastream, along with their functionalities and use cases.
What's included
6 videos1 assignment1 app item
This module focuses on data extraction and loading processes on Google Cloud, particularly with BigQuery. It covers the basic extraction and loading architecture, the bq command-line tool, BigQuery Data Transfer Service, and BigLake as an alternative to traditional extract-load patterns.
What's included
6 videos1 assignment1 app item
This module provides an overview of ELT (extract, load, transform) processes on Google Cloud. It covers the basic ELT architecture, a common ELT pipeline example, BigQuery's capabilities for scripting and scheduling SQL, and the functionality and use cases of Dataform.
What's included
5 videos1 assignment1 app item
This module provides an overview of ETL (extract, transform, load) processes on Google Cloud. It covers the basic ETL architecture, GUI tools, batch and streaming data processing options (Dataproc, Dataproc Serverless), and the role of Bigtable in data pipelines.
What's included
8 videos1 assignment2 app items
This module focuses on automation patterns and options for pipelines on Google Cloud. It covers various tools and services like Cloud Scheduler, Workflows, Cloud Composer, Cloud Run functions, and Eventarc, along with their functionalities and use cases for automation.
What's included
7 videos1 assignment1 app item
In this final section, we review what was presented in this course and discuss the next steps to continue your cloud learning journey.
What's included
1 video1 reading
Instructor
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Recommended if you're interested in Cloud Computing
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.