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
A Data Engineer designs and builds systems that collect and transform the data used to inform business decisions.
A Data Engineer designs and builds systems that collect and transform the data used to inform business decisions. This learning path guides you through a curated collection of on-demand courses, labs, and skill badges that provide you with real-world, hands-on experience using Google Cloud technologies essential to the Data Engineer role. Once you complete the path, check out the Google Cloud Data Engineer certification to take the next steps in your professional journey.
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
Mit dem Skill-Logo zum Kurs Build a Data Warehouse with BigQuery weisen Sie fortgeschrittene Kenntnisse in folgenden Bereichen nach: Daten zusammenführen, um neue Tabellen zu erstellen, Probleme mit Joins lösen, Daten mit Unions anhängen, nach Daten partitionierte Tabellen erstellen und...
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 - Deutsch
In diesem Kurs wird gezeigt, wie Sie KI-/ML-Modelle für Aufgaben basierend auf generativer KI in BigQuery verwenden. Anhand eines praktischen Anwendungsfalls zum Customer-Relationship-Management lernen Sie den Workflow zur Lösung eines Geschäftsproblems mit Gemini-Modellen kennen. Zur besseren Nachvollziehbarkeit bietet der Kurs...