Course Overview
Data Engineers design solutions that ensure maximum flexibility and scalability, while meeting all required security controls.
Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning.
This Google Cloud course covers structured, unstructured, and streaming data.
Limited Time Offer: Get up to 45% OFF selected Google Cloud courses in H2 2025 via our Google Cloud Certified program.
What are the skills covered
- Design and build data processing systems on Google Cloud.
- Process batch and streaming data by implementing autoscaling data pipelines on Dataflow.
- Derive business insights from extremely large datasets using BigQuery.
- Leverage unstructured data using Spark and ML APIs on Dataproc.
- Enable instant insights from streaming data.
Who should attend this course
This class is intended for experienced developers who are responsible for managing big data transformations including:
- Extracting, loading, transforming, cleaning, and validating data.
- Designing pipelines and architectures for data processing.
- Creating and maintaining machine learning and statistical models.
- Querying datasets, visualizing query results and creating reports
Course Curriculum
What are the Prerequisites
To get the most of out of this course, participants should have:
- Prior Google Cloud experience using Cloud Shell and accessing products from the Google Cloud console.
- Basic proficiency with a common query language such as SQL.
- Experience with data modeling and ETL (extract, transform, load) activities.
- Experience developing applications using a common programming language such as Python.
Course Modules
Exam & Certification
Google Cloud Professional Data Engineer Certification
A Google Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
