ARUL JERALD J
Учасник із 2023
Золота ліга
Кількість балів: 24425
Enrich Metadata and Discovery of Lakehouse Data
Earned жовт. 22, 2024 EDT
Streaming Analytics into BigQuery
Earned жовт. 22, 2024 EDT
Secure Lakehouse Data
Earned жовт. 22, 2024 EDT
Monitoring in Google Cloud
Earned жовт. 21, 2024 EDT
DEPRECATED Networking Fundamentals on Google Cloud
Earned жовт. 21, 2024 EDT
Monitor and Manage Google Cloud Resources
Earned жовт. 16, 2024 EDT
Integrate BigQuery Data and Google Workspace using Apps Script
Earned жовт. 16, 2024 EDT
Build and Deploy a Generative AI solution using a RAG framework
Earned серп. 23, 2024 EDT
Visualize Your Data in Looker
Earned серп. 14, 2024 EDT
BI Reporting: Looker Visualization on BigQuery
Earned серп. 14, 2024 EDT
Develop Agent Search and Conversation Applications
Earned серп. 13, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud - Yкраїнська
Earned серп. 13, 2024 EDT
Text Prompt Engineering Techniques
Earned серп. 13, 2024 EDT
[DEPRECATED] Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned серп. 1, 2024 EDT
Підготовка даних для інтерфейсів API машинного навчання в Google Cloud
Earned черв. 6, 2024 EDT
Create ML Models with BigQuery ML
Earned лист. 17, 2023 EST
Підготовка даних для інтерфейсів API машинного навчання в Google Cloud
Earned лист. 9, 2023 EST
Налаштування Cloud Load Balancing для Compute Engine
Earned лист. 9, 2023 EST
Applying Advanced LookML Concepts in Looker
Earned серп. 31, 2023 EDT
Technology + Beyond the UI
Earned серп. 31, 2023 EDT
Technology + Within the UI
Earned серп. 31, 2023 EDT
Delivery Shadowing
Earned серп. 31, 2023 EDT
Developing Data Models with LookML
Earned серп. 31, 2023 EDT
Case Studies
Earned серп. 30, 2023 EDT
Liquid Templates and Parameters
Earned серп. 30, 2023 EDT
Extends to Keep LookML DRY
Earned серп. 29, 2023 EDT
Version Control and Caching
Earned серп. 29, 2023 EDT
Achieving Business Outcomes with Looker
Earned серп. 29, 2023 EDT
Driving Data Culture and Designing Dashboards
Earned серп. 28, 2023 EDT
Building Reports in Looker
Earned серп. 28, 2023 EDT
Admin Roles and Folder Access
Earned серп. 28, 2023 EDT
The Modern Data Platform and LookML
Earned серп. 28, 2023 EDT
Manage Data Models in Looker
Earned серп. 24, 2023 EDT
Understanding LookML in Looker
Earned серп. 24, 2023 EDT
Build LookML Objects in Looker
Earned серп. 23, 2023 EDT
Prepare Data for Looker Dashboards and Reports
Earned серп. 22, 2023 EDT
Table Calculations, Pivots, and Visualizations
Earned серп. 21, 2023 EDT
Analyzing and Visualizing Data in Looker
Earned серп. 21, 2023 EDT
Looker Explained
Earned серп. 18, 2023 EDT
Complete the Enrich Metadata and Discovery of Lakehouse Data skill badge course to demonstrate skills in BigQuery, Lakehouse, and Knowledge Catalog. You create Lakehouse tables and enrich metadata management and discovery of the table data.
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Earn a skill badge by completing the Streaming Analytics into BigQuery skill badge course, where you use Pub/Sub, Dataflow and BigQuery together to stream data for analytics.
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Complete the introductory Secure Lakehouse Data skill badge course to demonstrate skills with IAM, BigQuery, Lakehouse, and Knowledge Catalog to create and secure Lakehouse tables.
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Complete the introductory Monitoring in Google Cloud skill badge course to demonstrate skills in the following: using Cloud Monitoring tools to monitor resources on Google Cloud.
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Earn a skill badge by completing the Networking Fundamentals on Google Cloud quest, where you learn how to work with VPC networks and load balancers on Google Cloud. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this Skill Badge, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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Complete the introductory Monitor and Manage Google Cloud Resources skill badge to demonstrate skills in the following: granting and revoking IAM permissions; installing monitoring and logging agents; creating, deploying, and testing an event-driven Cloud Run function.
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Complete the introductory Integrate BigQuery Data and Google Workspace using Apps Script skill badge course to demonstrate skills in connecting Workspace products with BigQuery via AppSheet.
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Demonstrate your ability to implement updated prompt engineering techniques and utilize several of Gemini's key capacilities including multimodal understanding and function calling. Then integrate generative AI into a RAG application deployed to Cloud Run. This course contains labs that are to be used as a test environment. They are deployed to test your understanding as a learner with a limited scope. These technologies can be used with fewer limitations in a real world environment.
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This skill badge course aims to unlock the power of data visualization and business intelligence reporting with Looker, and gain hands-on experience through labs.
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This workload aims to upskill Google Cloud partners to perform specific tasks for modernization using LookML on BigQuery. A proof-of-concept will take learners through the process of creating LookML visualizations on BigQuery. During this course, learners will be guided specifically on how to write Looker modeling language, also known as LookML and create semantic data models, and learn how LookML constructs SQL queries against BigQuery. At a high level, this course will focus on basic LookML to create and access BigQuery objects, and optimize BigQuery objects with LookML.
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In this quest, you’ll use text embeddings for tasks like classification, outlier detection, text clustering and semantic search. You’ll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) solutions, such as for question-answering systems, using Google Cloud’s Vertex AI and Google Cloud databases.
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Що більше штучний інтелект і машинне навчання використовуються в корпоративних середовищах, то нагальнішою стає потреба розробити принципи відповідального ставлення до них. Однак говорити про принципи відповідального використання штучного інтелекту легше, ніж застосовувати їх на практиці. Цей курс допоможе вам дізнатись, як запровадити відповідальну роботу зі штучним інтелектом у вашій організації.
У цьому курсі ви дізнаєтеся про підхід Google Cloud до відповідального використання ШІ, а також отримаєте практичні поради й набудете досвіду, який допоможе вам розробити власний підхід до цього завдання.
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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.
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This course on Integrate Vertex AI Search and Conversation into Voice and Chat Apps is composed of a set of labs to give you a hands on experience to interacting with new Generative AI technologies. You will learn how to create end-to-end search and conversational experiences by following examples. These technologies complement predefined intent-based chat experiences created in Dialogflow with LLM-based, generative answers that can be based on your own data. Also, they allow you to porvide enterprise-grade search experiences for internal and external websites to search documents, structure data and public websites.
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Пройдіть вступний кваліфікаційний курс Підготовка даних для інтерфейсів API машинного навчання в Google Cloud, щоб продемонструвати свої навички щодо очистки даних за допомогою сервісу Dataprep by Trifacta, запуску конвеєрів даних у Dataflow, створення кластерів і запуску завдань Apache Spark у Managed Service for Apache Spark, а також виклику API машинного навчання, зокрема Cloud Natural Language API, Google Cloud Speech-to-Text API і Video Intelligence API.
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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.
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Пройдіть вступний кваліфікаційний курс Підготовка даних для інтерфейсів API машинного навчання в Google Cloud, щоб продемонструвати свої навички щодо очистки даних за допомогою сервісу Dataprep by Trifacta, запуску конвеєрів даних у Dataflow, створення кластерів і запуску завдань Apache Spark у Managed Service for Apache Spark, а також виклику API машинного навчання, зокрема Cloud Natural Language API, Google Cloud Speech-to-Text API і Video Intelligence API.
Докладніше
Пройдіть вступний кваліфікаційний курс Налаштування Cloud Load Balancing для Compute Engine, щоб продемонструвати свої навички: створення й розгортання віртуальних машин у Compute Engine; налаштування мережі й розподілювачів навантаження додатків.
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In this course, you will get hands-on experience applying advanced LookML concepts in Looker. You will learn how to use Liquid to customize and create dynamic dimensions and measures, create dynamic SQL derived tables and customized native derived tables, and use extends to modularize your LookML code.
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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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In this course, you shadow a series of client meetings led by a Looker Professional Services Consultant.
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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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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 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 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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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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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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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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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 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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Complete the intermediate Manage Data Models in Looker skill badge course to demonstrate skills in the following: maintaining LookML project health; utilizing SQL runner for data validation; employing LookML best practices; optimizing queries and reports for performance; and implementing persistent derived tables and caching policies.
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In this course, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.
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Complete the introductory Build LookML Objects in Looker skill badge course to demonstrate skills in the following: building new dimensions and measures, views, and derived tables; setting measure filters and types based on requirements; updating dimensions and measures; building and refining Explores; joining views to existing Explores; and deciding which LookML objects to create based on business requirements.
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Complete the introductory Prepare Data for Looker Dashboards and Reports skill badge course to demonstrate skills in the following: filtering, sorting, and pivoting data; merging results from different Looker Explores; and using functions and operators to build Looker dashboards and reports for data analysis and visualization.
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This course reviews the processes for creating table calculations, pivots and visualizations
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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.
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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.
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