Cloud Architecture: Design, Implement, and Manage
Earned août 12, 2024 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned juin 10, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned mai 26, 2024 EDT
Implementing Cloud Load Balancing for Compute Engine
Earned mai 23, 2024 EDT
Build a Data Warehouse with BigQuery
Earned mai 23, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned mai 19, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned nov. 6, 2023 EST
Serverless Data Processing with Dataflow: Foundations
Earned oct. 26, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned oct. 25, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned oct. 8, 2023 EDT
Earn a skill badge by completing the Cloud Architecture: Design, Implement, and Manage to demonstrate skills in the following: deploy a publicly accessible website using Apache web servers, configure a Compute Engine VM using startup scripts, configure secure RDP using a Windows Bastion host and firewall rules, build and deploy a Docker image to a Kubernetes cluster and then update it, and create a CloudSQL instance and import a MySQL database. This skill badge is a great resource for understanding topics that will appear in the Google Cloud Certified Professional Cloud Architect certification exam.
En savoir plus
Google Cloud Fundamentals: Core Infrastructure introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.
En savoir plus
Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.
En savoir plus
Complete the introductory Implementing Cloud Load Balancing for Compute Engine skill badge to demonstrate skills in the following: creating and deploying virtual machines in Compute Engine and configuring network and application load balancers.
En savoir plus
Complete the intermediate Build a Data Warehouse with BigQuery skill badge course 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 BigQuery.
En savoir plus
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.
En savoir plus
While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
En savoir plus
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 Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
En savoir plus
Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
En savoir plus
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.
Cloud Architecture: Design, Implement, and Manage
Earned août 12, 2024 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned juin 10, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned mai 26, 2024 EDT
Implementing Cloud Load Balancing for Compute Engine
Earned mai 23, 2024 EDT
Build a Data Warehouse with BigQuery
Earned mai 23, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned mai 19, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned nov. 6, 2023 EST
Serverless Data Processing with Dataflow: Foundations
Earned oct. 26, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned oct. 25, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned oct. 8, 2023 EDT