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José Sotomayor

Учасник із 2022

Золота ліга

Кількість балів: 22940
Cloud Hero: PDE Continuing Education Renewal Earned вер. 22, 2026 EDT
Налаштування середовища для розробки додатка в Google Cloud Earned трав. 3, 2024 EDT
Secure Lakehouse Data Earned жовт. 26, 2023 EDT
Security & Identity Fundamentals Earned жовт. 24, 2023 EDT
Налаштування середовища для розробки додатка в Google Cloud Earned жовт. 22, 2023 EDT
Create a Secure Data Lake on Cloud Storage Earned жовт. 22, 2023 EDT
Початок роботи з інфраструктурою Earned жовт. 22, 2023 EDT
Розробка хмарних технологій Earned жовт. 18, 2023 EDT
Implement Cloud Security Fundamentals on Google Cloud Earned жовт. 18, 2023 EDT
Enrich Metadata and Discovery of Lakehouse Data Earned жовт. 12, 2023 EDT
Developing Applications with Cloud Run Functions on Google Cloud Earned вер. 9, 2023 EDT
Build Serverless Applications with Cloud Run Functions Earned вер. 1, 2023 EDT
Service Orchestration and Choreography on Google Cloud Earned серп. 21, 2023 EDT
Building Applications with Eventarc on Google Cloud Earned серп. 16, 2023 EDT
Build Event-Driven Applications with Eventarc Earned серп. 12, 2023 EDT
Створення сітки даних за допомогою Knowledge Catalog Earned серп. 3, 2023 EDT
Organize and Govern Data with Knowledge Catalog Earned серп. 2, 2023 EDT
Build a Certification Study Guide: PDE Exam Prep Earned лип. 25, 2023 EDT
Serverless Data Processing with Dataflow: Operations Earned лип. 25, 2023 EDT
Engineer Data for Predictive Modeling with BigQuery ML Earned лип. 8, 2023 EDT
Підготовка даних для інтерфейсів API машинного навчання в Google Cloud Earned лип. 8, 2023 EDT
Serverless Data Processing with Dataflow: Develop Pipelines Earned лип. 7, 2023 EDT
Build a Data Warehouse with BigQuery Earned черв. 22, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned черв. 17, 2023 EDT
Build Streaming Data Pipelines on Google Cloud Earned черв. 11, 2023 EDT
Serverless Data Processing with Dataflow: Foundations Earned черв. 8, 2023 EDT
Analyzing and Visualizing Data in Looker - Locales Earned трав. 25, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals - українська Earned бер. 2, 2023 EST
Build Batch Data Pipelines on Google Cloud Earned лют. 12, 2023 EST
Build Data Lakes and Data Warehouses on Google Cloud Earned жовт. 17, 2022 EDT

Renew your Professional Data Engineer certification through hands-on labs and friendly competition! This Cloud Hero challenge puts your skills to the test, putting your expertise into practice across two required labs. Earn points for accuracy, rack up bonus points for speed, and see where you stand on the leaderboard. Can you claim the top spot? **Critical Steps for Recertification:** To successfully recertify, you must link your Google Skills and CM Connect accounts, complete both labs in this game, and manually enroll in two required courses. How to enroll / steps (can be done before or after the game): Visit CertMetrics and click "Sign in with Google Skills" Click each course link below and click the "Start" button on the course page to enroll Course 1 Course 2 Come back to this page to start this game Complete the labs in the game to earn your renewal

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Щоб отримати кваліфікаційний значок, пройдіть курс Налаштування середовища для розробки додатка в Google Cloud. У ньому ви навчитеся створювати й підключати хмарну інфраструктуру, спрямовану на зберігання даних, за допомогою базових можливостей таких технологій, як Cloud Storage, система керування ідентифікацією і доступом, Cloud Functions та Pub/Sub.

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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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Security is an uncompromising feature of Google Cloud services, and Google Cloud has developed specific tools for ensuring safety and identity across your projects. In this fundamental-level quest, you will get hands-on practice with Google Cloud’s Identity and Access Management (IAM) service, which is the go-to for managing user and virtual machine accounts. You will get experience with network security by provisioning VPCs and VPNs, and learn what tools are available for security threat and data loss protections.

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Щоб отримати кваліфікаційний значок, пройдіть курс Налаштування середовища для розробки додатка в Google Cloud. У ньому ви навчитеся створювати й підключати хмарну інфраструктуру, спрямовану на зберігання даних, за допомогою базових можливостей таких технологій, як Cloud Storage, система керування ідентифікацією і доступом, Cloud Functions та Pub/Sub.

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Complete the introductory Create a Secure Data Lake on Cloud Storage skill badge course to demonstrate skills in the following: securing and configuring a Cloud Storage bucket, using Gemini for text generation, managing IAM access control, and establishing a Knowledge Catalog lake for data governance.

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Якщо ви лише пробуєте розробляти хмарні рішення й шукаєте практичні заняття на додаток до кваліфікаційного курсу "Знайомство з Google Cloud", тоді цей курс саме для вас. Ви отримаєте прикладний досвід завдяки практичним заняттям, присвяченим Cloud Storage і іншим ключовим сервісам додатків, як-от Monitoring і Cloud Functions. Ви отримаєте цінні навички, які можна застосовувати в будь-яких проєктах Google Cloud.

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Цей вступний курс унікальний серед інших курсів. Практичні заняття розроблено так, щоб ІТ-фахівці могли опрацювати теми й сервіси, які ввійдуть до сертифікації Google Cloud Certified Associate Cloud Engineer. У цей курс входять спеціально розроблені практичні заняття, зокрема присвячені керуванню ідентифікацією і доступом, організації мереж і розгортанню Kubernetes Engine, щоб ви могли перевірити свої знання про Google Cloud. Майте на увазі, що хоча виконання цих практичних занять покращить ваші навички й уміння, радимо також переглянути посібник із підготовки до іспиту й інші доступні ресурси.

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Complete the intermediate Implement Cloud Security Fundamentals on Google Cloud skill badge course to demonstrate skills in the following: creating and assigning roles with Identity and Access Management (IAM); creating and managing service accounts; enabling private connectivity across virtual private cloud (VPC) networks; restricting application access using Identity-Aware Proxy; managing keys and encrypted data using Cloud Key Management Service (KMS); and creating a private Kubernetes cluster.

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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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In this course, you learn about Cloud Run functions, Google's serverless, fully-managed functions as a service (FaaS) product that lets you implement single-purpose function code that reponds to HTTP requests and events from your cloud infrastructure.

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Earn a Introductory skill badge by completing the Build Serverless Applications with Cloud Run Functions course, where you learn how to use Cloud Run functions through the Google Cloud console and on the command line.

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This course introduces you to event-based applications and teaches you how to use service orchestration and choreography to coordinate microservices. Using lectures and hands-on labs, you learn how to use Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler to build microservices applications on Google Cloud.

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This course teaches you how to build and secure event-driven applications by using Eventarc. Using lectures and hands-on labs, you create Eventarc triggers to route events from Google Cloud services to event receivers that act on the events. You learn about the standard CloudEvents format that is used by Eventarc, and you build and deploy an event receiver service to consume events.

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Earn a Skill Badge by completing the Build Event-Driven Applications with Eventarc course. In this course, you use Eventarc to create event triggers for different resources, including Pub/Sub topics and Cloud Storage buckets.

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Пройдіть вступний кваліфікаційний курс Створення сітки даних за допомогою Knowledge Catalog, щоб продемонструвати свої навички створення такої сітки для покращеної безпеки даних, керування ними й пошуку в Google Cloud. Ви потренуєтеся й перевірите свої навички щодо позначення тегами об’єктів, призначення ролей IAM і перевірки якості даних у Knowledge Catalog.

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Complete the introductory Organize and Govern Data with Knowledge Catalog skill badge to demonstrate skills in the following: creating Knowledge Catalog assets, creating aspect types, and applying aspects to entries in Knowledge Catalog.

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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Data Engineer certification exam. You'll review Gemini Notebook features, create a notebook in Gemini Notebook, and learn how to use a study guide to practice for a certification exam.

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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 practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.

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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.

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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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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, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

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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.

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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.

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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.

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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.

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This course, Analyzing and Visualizing Data in Looker - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Analyzing and Visualizing Data in Looker. 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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Під час курсу ви зможете ознайомитися з продуктами й сервісами Google Cloud для роботи з масивами даних і машинним навчанням, які підтримують життєвий цикл роботи з даними для тренування моделей штучного інтелекту. У курсі розглядаються процеси, проблеми й переваги створення конвеєру масиву даних і моделей машинного навчання з Vertex AI у Google Cloud.

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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.

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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.

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