01
A Tour of Google Cloud Hands-on Labs
In this first hands-on lab you will access the Google Cloud console and use these basic Google Cloud features: Projects, Resources, IAM Users, Roles, Permissions, and APIs.
Data Engineer Learning Path
Advance your career in data engineering and prepare for the Google Cloud Professional Data Engineer Certification exam with this program.
The Google Cloud Professional Data Engineer Certification Program is a comprehensive program that provides the skills you need to advance your career and provides training to help you prepare for the industry-recognized Google Cloud Professional Data Engineer Certification exam administered by Google Cloud.
Recognized as the #1 top-paying certification in 2021 by Global Knowledge, this program is specifically designed for those aspiring to advance in the thriving field of data engineering. 87% of Google Cloud certified users feel more confident in their cloud skills.
In this program, you will learn about the fundamental principles and applications of Big Data and Machine Learning products within Google Cloud. Master how to use BigQuery for interactive data analysis, apply Cloud SQL and Dataproc for migrating existing MySQL and Hadoop/Pig/Spark/Hive tasks to Google Cloud, and differentiate between various data processing products on Google Cloud. You will have the opportunity to comprehend theoretical knowledge fully and apply it in practical situations, making use of Google Cloud Platform's vast range of tools for Real Time Data, Dataflow, and TensorFlow.
Applied Learning Project:
- This program incorporates hands-on labs using our Qwiklabs platform. These hands-on components will let you apply the skills you learn with Google Cloud Platform products and Google BigQuery.
02
Preparing for your Professional Data Engineer Journey
This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
03
Introduction to Data Engineering on Google Cloud
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.
04
Modernizing Data Lakes and Data Warehouses with Google Cloud
The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this...
05
Building Batch Data Pipelines on Google Cloud
Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course...
06
Building Resilient Streaming Analytics Systems on Google Cloud
Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course...
07
Serverless Data Processing with Dataflow: Foundations
This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache...
08
Serverless Data Processing with Dataflow: Develop Pipelines
In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows,...
09
Serverless Data Processing with Dataflow: Operations
In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best...
10
Build a Data Warehouse with BigQuery
Complete the intermediate Build a Data Warehouse with BigQuery skill badge to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in...
11
Build a Data Mesh with Dataplex
Complete the introductory Build a Data Mesh with Dataplex skill badge to demonstrate skills in the following: building a data mesh with Dataplex to facilitate data security, governance, and discovery on Google Cloud. You practice and test your skills in...
12
Boost Productivity with Gemini in BigQuery
This course explores Gemini in BigQuery, a suite of AI-driven features to assist data-to-AI workflow. These features include data exploration and preparation, code generation and troubleshooting, and workflow discovery and visualization. Through conceptual explanations, a practical use case, and hands-on...
13
Work with Gemini Models in BigQuery
This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course...