Essential Google Cloud Infrastructure: Foundation
Earned sept. 29, 2024 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned sept. 23, 2024 EDT
Preparing for Your Associate Cloud Engineer Journey
Earned sept. 19, 2024 EDT
Case Studies
Earned sept. 13, 2024 EDT
Delivery Shadowing
Earned sept. 13, 2024 EDT
Technology + Beyond the UI
Earned sept. 9, 2024 EDT
Technology + Within the UI
Earned sept. 4, 2024 EDT
Table Calculations, Pivots, and Visualizations
Earned août 29, 2024 EDT
Building Reports in Looker
Earned août 27, 2024 EDT
Liquid Templates and Parameters
Earned août 27, 2024 EDT
Extends to Keep LookML DRY
Earned août 26, 2024 EDT
Admin Roles and Folder Access
Earned août 26, 2024 EDT
Version Control and Caching
Earned août 26, 2024 EDT
The Modern Data Platform and LookML
Earned août 23, 2024 EDT
Driving Data Culture and Designing Dashboards
Earned août 16, 2024 EDT
Achieving Business Outcomes with Looker
Earned août 14, 2024 EDT
Looker Explained
Earned août 12, 2024 EDT
Complete the intermediate Implement Multimodal Vector Search with BigQuery skill badge to demonstrate skills in the following: using Gemini in BigQuery to generate and debug SQL, conduct sentiment analysis, summarize text and identify keywords, generate embeddings, create a Retrieval Augmented Generation (RAG) pipeline, and implement multimodal vector search.
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This course explores a Retrieval Augmented Generation (RAG) solution in BigQuery to mitigate AI hallucinations. It introduces a RAG workflow that encompasses creating embeddings, searching a vector space, and generating improved answers. The course explains the conceptual reasons behind these steps and their practical implementation with BigQuery. By the end of the course, learners will be able to build a RAG pipeline using BigQuery and generative AI models like Gemini and embedding models to address their own AI hallucination use cases.
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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 also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.
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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 labs, the course empowers data practitioners to boost their productivity and expedite the development pipeline.
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Welcome to Observability in Google Cloud, the second part of a two-part course series. It is suggested that you complete part 1, Logging and Monitoring in Google Cloud, prior to taking this course.
This course is all about application performance management tools, including Error Reporting, Cloud Trace, and Cloud Profiler.
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Welcome to the two-part course on Logging, Monitoring, and Observability in Google Cloud. The core operations tools in Google Cloud break down into two major categories. The operations-focused components and the application performance management tools. This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring. After taking this course, it is suggested that you complete part 2, Observability in Google Cloud, to learn about the available application performance management tools.
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This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.
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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including securely interconnecting networks, load balancing, autoscaling, infrastructure automation and managed services.
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Welcome to the Getting Started with Google Kubernetes Engine course. If you're interested in Kubernetes, a software layer that sits between your applications and your hardware infrastructure, then you’re in the right place! Google Kubernetes Engine brings you Kubernetes as a managed service on Google Cloud.
The goal of this course is to introduce the basics of Google Kubernetes Engine, or GKE, as it’s commonly referred to, and how to get applications containerized and running in Google Cloud. The course starts with a basic introduction to Google Cloud, and is then followed by an overview of containers and Kubernetes, Kubernetes architecture, and Kubernetes operations.
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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Compute Engine. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems and applications services. This course also covers deploying practical solutions including customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring.
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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Compute Engine. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, virtual machines and applications services. You will learn how to use the Google Cloud through the console and Cloud Shell. You'll also learn about the role of a cloud architect, approaches to infrastructure design, and virtual networking configuration with Virtual Private Cloud (VPC), Projects, Networks, Subnetworks, IP addresses, Routes, and Firewall rules.
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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.
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This course helps you structure your preparation for the Associate Cloud Engineer exam. You will learn about the Google Cloud domains covered by the exam and how to create a study plan to improve your domain knowledge.
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By the end of this course, you should feel confident employing technical concepts to fulfill business requirements and be familiar with common complex design patterns.
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In this course, you shadow a series of client meetings led by a Looker Professional Services Consultant.
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In this course you will discover additional tools for your toolbox for working with complex deployments, building robust solutions, and delivering even more value.
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Develop technical skills beyond LookML along with basic administration for optimizing Looker instances
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This course reviews the processes for creating table calculations, pivots and visualizations
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This course is designed for Looker users who want to create their own ad-hoc reports. It assumes experience of everything covered in our Get Started with Looker course (logging in, finding Looks & dashboards, adjusting filters, and sending data)
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In this course you will discover Liquid, the templating language invented by Shopify and explore how it can be used in Looker to create dynamic links, content, formatting, and more.
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Hands on course covering the main uses of extends and the three primary LookML objects extends are used on as well as some advanced usage of extends.
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This course is designed to teach you about roles, permission sets and model sets. These are areas that are used together to manage what users can do and what they can see in Looker.
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This course aims to introduce you to the basic concepts of Git: what it is and how it's used in Looker. You will also develop an in-depth knowledge of the caching process on the Looker platform, such as why they are used and why they work
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This course provides an introduction to databases and summarized the differences in the main database technologies. This course will also introduce you to Looker and how Looker scales as a modern data platform. In the lessons, you will build and maintain standard Looker data models and establish the foundation necessary to learn Looker's more advanced features.
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This course provides an iterative approach to plan, build, launch, and grow a modern, scalable, mature analytics ecosystem and data culture in an organization that consistently achieves established business outcomes. Users will also learn how to design and build a useful, easy-to-use dashboard in Looker. It assumes experience with everything covered in our Getting Started with Looker and Building Reports in Looker courses.
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In this course, we’ll show you how organizations are aligning their BI strategy to most effectively achieve business outcomes with Looker. We'll follow four iterative steps: Plan, Build, Launch, Grow, and provide resources to take into your own services delivery to build Looker with the goal of achieving business outcomes.
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By the end of this course, you should be able to articulate Looker's value propositions and what makes it different from other analytics tools in the market. You should also be able to explain how Looker works, and explain the standard components of successful service delivery.
Implement Multimodal Vector Search with BigQuery
Earned mars 31, 2025 EDT
Create Embeddings, Vector Search, and RAG with BigQuery
Earned mars 31, 2025 EDT
Work with Gemini Models in BigQuery
Earned mars 30, 2025 EDT
Boost Productivity with Gemini in BigQuery
Earned mars 29, 2025 EDT
Observability in Google Cloud
Earned nov. 6, 2024 EST
Logging and Monitoring in Google Cloud
Earned nov. 5, 2024 EST
Developing Data Models with LookML
Earned oct. 19, 2024 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation
Earned oct. 17, 2024 EDT
Getting Started with Google Kubernetes Engine
Earned oct. 14, 2024 EDT
Essential Google Cloud Infrastructure: Core Services
Earned oct. 2, 2024 EDT
Essential Google Cloud Infrastructure: Foundation
Earned sept. 29, 2024 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned sept. 23, 2024 EDT
Preparing for Your Associate Cloud Engineer Journey
Earned sept. 19, 2024 EDT
Case Studies
Earned sept. 13, 2024 EDT
Delivery Shadowing
Earned sept. 13, 2024 EDT
Technology + Beyond the UI
Earned sept. 9, 2024 EDT
Technology + Within the UI
Earned sept. 4, 2024 EDT
Table Calculations, Pivots, and Visualizations
Earned août 29, 2024 EDT
Building Reports in Looker
Earned août 27, 2024 EDT
Liquid Templates and Parameters
Earned août 27, 2024 EDT
Extends to Keep LookML DRY
Earned août 26, 2024 EDT
Admin Roles and Folder Access
Earned août 26, 2024 EDT
Version Control and Caching
Earned août 26, 2024 EDT
The Modern Data Platform and LookML
Earned août 23, 2024 EDT
Driving Data Culture and Designing Dashboards
Earned août 16, 2024 EDT
Achieving Business Outcomes with Looker
Earned août 14, 2024 EDT
Looker Explained
Earned août 12, 2024 EDT