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Quốc Hồ Bảo

Member since 2021

Diamond League

22605 points
Serverless Data Processing with Dataflow: Foundations Earned Eyl 8, 2024 EDT
Streaming Analytics into BigQuery Earned Eyl 2, 2024 EDT
Build a Data Warehouse with BigQuery Earned Eyl 2, 2024 EDT
BigQuery Verilerinden Analiz Elde Etme Earned Eyl 1, 2024 EDT
Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama Earned Eyl 1, 2024 EDT
Engineer Data for Predictive Modeling with BigQuery ML Earned Ağu 18, 2024 EDT
Manage Kubernetes in Google Cloud Earned Nis 20, 2024 EDT
Generative AI Explorer - Agent Platform Earned Mar 23, 2024 EDT
Build Batch Data Pipelines on Google Cloud Earned Mar 18, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Şub 9, 2024 EST
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned Şub 7, 2024 EST
Build a Certification Study Guide: PDE Exam Prep Earned Şub 5, 2024 EST

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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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 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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Giriş düzeyindeki BigQuery Verilerinden Analiz Elde Etme beceri rozetini alarak şu konulardaki becerilerinizi gösterin: SQL sorguları yazma, herkese açık tabloları sorgulama, örnek verileri BigQuery'ye yükleme, BigQuery'deki sorgu doğrulayıcı ile yaygın söz dizimi sorunlarını giderme ve BigQuery verilerine bağlanarak Data Studio'da rapor oluşturma.

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Giriş düzeyindeki Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama beceri rozetini tamamlayarak şu konulardaki becerilerinizi gösterin: Dataprep by Trifacta ile veri temizleme, Dataflow'da veri ardışık düzenleri çalıştırma, Managed Service for Apache Spark'ta küme oluşturma ve Apache Spark işleri çalıştırma ve makine öğrenimi API'lerini (Cloud Natural Language API, Google Cloud Speech-to-Text API ve Video Intelligence API dahil olmak üzere) çağırma.

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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 Manage Kubernetes in Google Cloud skill badge course to demonstrate skills in the following: managing deployments with kubectl, monitoring and debugging applications on Google Kubernetes Engine (GKE), and continuous delivery techniques.

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The Generative AI Explorer - Agent Platform course is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the Gemini models in Agent Platform. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via custom training in Agent Platform and deploy it to an endpoint in Agent Platform.

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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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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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