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Pravin Shende

Учасник із 2025

Діамантова ліга

Кількість балів: 69393
Data Warehousing for Partners: Process Data with Dataproc Earned лют. 26, 2026 EST
AWS EC2 to Compute Engine Earned лют. 13, 2026 EST
Modernization of Java Applications for Google Cloud Earned лют. 1, 2026 EST
Data Warehousing for Partners: Stream Data with Pub/Sub Earned жовт. 31, 2025 EDT
Teradata to BigQuery Earned вер. 27, 2025 EDT
BigQuery Migration Service Earned вер. 27, 2025 EDT
Data Warehousing for Partners: Streaming Analytics Earned лип. 23, 2025 EDT
Working with Notebooks in Vertex AI Earned лип. 21, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI Earned лип. 4, 2025 EDT
Introduction to Data Engineering on Google Cloud Earned черв. 11, 2025 EDT
Improve Vertex AI Search and Gemini Enterprise Search Results Earned трав. 20, 2025 EDT
Vertex AI Search and Gemini Enterprise Analytics Earned трав. 20, 2025 EDT
Vertex AI Search and Gemini Enterprise UI Configurations Earned трав. 20, 2025 EDT
Building Reports in Looker Earned трав. 17, 2025 EDT
Observability in Google Cloud Earned трав. 17, 2025 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation Earned трав. 17, 2025 EDT
Logging and Monitoring in Google Cloud Earned трав. 17, 2025 EDT
Початок роботи з інфраструктурою Earned трав. 14, 2025 EDT
Essential Google Cloud Infrastructure: Foundation Earned трав. 14, 2025 EDT
Migrating Hadoop Workloads Earned трав. 13, 2025 EDT
Inside Track: Dataflow Advanced Earned трав. 13, 2025 EDT
Serverless Data Processing with Dataflow: Foundations Earned трав. 13, 2025 EDT
Essential Google Cloud Infrastructure: Core Services Earned трав. 13, 2025 EDT
Data Warehousing for Partners: Design in BigQuery Earned трав. 10, 2025 EDT
Google Cloud Fundamentals: Core Infrastructure - Yкраїнська Earned трав. 9, 2025 EDT
Introduction to Large Language Models - Українська Earned бер. 11, 2025 EDT
Introduction to Generative AI - Українська Earned лют. 28, 2025 EST
Introduction to Responsible AI - Українська Earned лют. 28, 2025 EST

This course explores the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataproc.

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Migration from AWS EC2 to Google Cloud Compute Engine using Migrate to Virtual Machines (v5) using demo VM(s). It provides a proof-of-concept that walks you through the process of replicating a VM to doing test cutover and final cutover of the VM.

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This course aims to upskill Google Cloud partners to perform specific tasks in rehosting applications from on-premise to Google Cloud. It also aims to re-platform applications to run in GKE. Learners will perform the tasks of Migrating MySQL, Angular, and Java applications from their on-premise machines to Google Cloud VM instances. Sample code will be used during the migration.

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This course explores how to implement a streaming analytics solution using Pub/Sub.

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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of Migration from Teradata to BigQuery using the Data Transfer Service and the Teradata TPT Export Utility. Sample Data will be used during both methods. Learners will complete a challenge lab that focuses on the process of transferring both schema, data and SQL from a Teradata data warehouse to BigQuery.

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In this course, you explore the four components that make up the BigQuery Migration Service. They are Migration Assessment, SQL Translation, Data Transfer Service, and Data Validation. You will use each of these tools to perform a migration using to BigQuery.

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This course explores how to implement a streaming analytics solution using Dataflow and BigQuery.

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This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.

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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.

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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.

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If you've worked with data, you know that some data is more reliable than other data. In this course, you'll learn a variety of techniques to present the most reliable or useful results to your users. Create serving controls to boost or bury search results. Rank search results to ensure that each query is answered by the most relevant data. If needed, tune your search engine. Learn to measure search results to ensure your search applications deliver the best possible results to each user. (Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)

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AI Applications provides built-in analytics for your Vertex AI Search and Gemini Enterprise apps. Learn what metrics are tracked and how to view them in this course. (Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)

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Initial deployment of Vertex AI Search and Gemini Enterprise apps takes only a few clicks, but getting the configurations right can elevate a deployment from a basic off-the-shelf app to an excellent custom search or recommendations experience. In this course, you'll learn more about the many ways you can customize and improve search, recommendations, and Gemini Enterprise apps. (Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)

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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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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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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 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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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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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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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating workloads from Hadoop environments to corresponding Google Cloud services and hosted products. The following will addressed will be: The Hadoop ecosystem and products Hadoop architecture and post migration architectures to Google Cloud Assessment Data transfer options Workload migrations, namely: Spark to Dataproc Serverless, Apache Oozie to Composer (Airflow), and Hive to BigQuery Security and governance Logging and Monitoring

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This course provided technical training in Google Cloud Dataflow, the foundational pillar of Google Cloud's streaming analytics solution. This training is intended for Google Cloud technical experts that are looking to further their understanding of Dataflow to advance sales-related technical evaluations, customer implementations, technical support, and data processing applications. This course explores topics related to Dataflow, including: Apache Beam SDK Google Cloud Dataflow Runner Autoscaling Logic Sources / Sinks Schemas / Dataflow SQL Dynamic Work RebalancingMonitoring, Troubleshooting, and Optimization Testing and CI/CD

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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 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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Welcome to Design in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on schema design.

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Курс "Знайомство з Google Cloud: основна інфраструктура" охоплює важливі поняття й терміни щодо використання Google Cloud. Переглядаючи відео й виконуючи практичні завдання, слухачі ознайомляться з різними сервісами Google Cloud для обчислень і зберігання даних, а також важливими ресурсами й інструментами для керування правилами. Крім того, вони зможуть їх порівнювати.

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У цьому ознайомлювальному курсі мікронавчання ви дізнаєтеся, що таке великі мовні моделі, де вони використовуються і як підвищити їх ефективність коригуванням запитів. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучного інтелекту.

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Це ознайомлювальний курс мікронавчання, який має пояснити, що таке генеративний штучний інтелект, як він використовується й чим відрізняється від традиційних методів машинного навчання. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучногоінтелекту.

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Це ознайомлювальний курс мікронавчання, який має пояснити, що таке відповідальне використання штучного інтелекту, чому воно важливе і як компанія Google реалізує його у своїх продуктах. Крім того, у цьому курсі викладено 7 принципів Google щодо штучного інтелекту.

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