Nguyen Minh Nhat
Jest członkiem od 2022
Introduction to Generative AI - Polski
Earned cze 6, 2023 EDT
Serverless Data Processing with Dataflow: Operations
Earned mar 16, 2023 EDT
Serverless Data Processing with Dataflow: Develop Pipelines
Earned mar 15, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned mar 15, 2023 EDT
Build Streaming Data Pipelines on Google Cloud
Earned mar 15, 2023 EDT
Build Batch Data Pipelines on Google Cloud
Earned mar 15, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned mar 14, 2023 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals
Earned mar 14, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned sie 29, 2022 EDT
Build a Certification Study Guide: PDE Exam Prep
Earned sie 28, 2022 EDT
[DEPRECATED] Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Earned kwi 10, 2022 EDT
Konfigurowanie środowiska programistycznego w Google Cloud
Earned kwi 10, 2022 EDT
Scaling with Google Cloud Operations
Earned kwi 9, 2022 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned kwi 9, 2022 EDT
Google Cloud Solutions I: Scaling Your Infrastructure
Earned kwi 9, 2022 EDT
Develop Serverless Applications on Cloud Run
Earned kwi 9, 2022 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned kwi 8, 2022 EDT
Build Infrastructure with Terraform on Google Cloud
Earned kwi 8, 2022 EDT
Build a Data Warehouse with BigQuery
Earned kwi 7, 2022 EDT
Celem tego szybkiego szkolenia dla początkujących jest wyjaśnienie, czym jest generatywna AI oraz jakie są jej zastosowania. Szkolenie przedstawia również różnice pomiędzy tą technologią a tradycyjnymi systemami uczącymi się, a także narzędzia Google, które pomogą Ci tworzyć własne aplikacje korzystające z generatywnej AI.
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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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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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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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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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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.
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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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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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Szkolenie Podstawy przetwarzania danych w Google Cloud pozwoli osobom z niewielkim
lub zerowym doświadczeniem z zakresu przetwarzania danych w chmurze szczegółowo
zapoznać się z najważniejszymi pojęciami z zakresu podstaw chmury, big data i systemów uczących się.
Zawiera także informacje o tym, gdzie i jak można wykorzystać Google Cloud.
Po zakończeniu szkolenia uczestnicy będą potrafili wyjaśnić pojęcia dotyczące przetwarzania danych w chmurze,
big data i systemów uczących się oraz zademonstrować wybrane umiejętności praktyczne.
To szkolenie należy do serii szkoleń o nazwie Google Cloud Computing Foundations (Podstawy usług w chmurze Google).
Szkolenia należy ukończyć w następującej kolejności:
Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales
Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Google Cloud Computing Foundations: Networking and Security in Google Cloud - Locales
Google Cloud Computing Foundation…
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Aby zdobyć odznakę umiejętności, ukończ szkolenie Konfigurowanie środowiska programistycznego w Google Cloud, w trakcie którego dowiesz się, jak utworzyć i podłączyć infrastrukturę w chmurzę do przechowywania danych przy użyciu podstawowych funkcji technologii Cloud Storage, Identity and Access Management, Cloud Functions oraz Pub/Sub.
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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task. This course explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts.
As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Many traditional enterprises use legacy systems and apps that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems and investing in new products and services. This course explores these challenges and offers solutions to overcome them by using cloud technology.
As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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In this course you will learn how you to harness serious Google Cloud power and infrastructure. The hands-on labs will give you use cases and you will be tasked with implementing scaling practices utilized by Google’s very own Solutions Architecture team. From developing enterprise grade load balancing and autoscaling, to building continuous delivery pipelines, Google Cloud Solutions I: Scaling your Infrastructure will teach you best practices for taking your Google Cloud projects to the next level.
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Complete the intermediate Develop Serverless Applications on Cloud Run skill badge course to demonstrate skills in the following: integrating Cloud Run with Cloud Storage for data management, architecting resilient asynchronous systems using Cloud Run and Pub/Sub, constructing REST API gateways powered by Cloud Run, and building and deploying services on Cloud Run.
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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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Complete the intermediate Build Infrastructure with Terraform on Google Cloud skill badge to demonstrate skills in the following: Infrastructure as Code (IaC) principles using Terraform, provisioning and managing Google Cloud resources with Terraform configurations, effective state management (local and remote), and modularizing Terraform code for reusability and organization.
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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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