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Pawan Dubey

Member since 2024

Diamond League

17860 points
Cloud Hero: PDE Continuing Education Renewal Earned Eyl 17, 2026 EDT
Build a Certification Study Guide: PDE Exam Prep Earned Tem 2, 2024 EDT
Streaming Analytics into BigQuery Earned May 2, 2024 EDT
Build a Data Warehouse with BigQuery Earned Nis 30, 2024 EDT
Build Batch Data Pipelines on Google Cloud Earned Nis 30, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Nis 18, 2024 EDT
BigQuery Verilerinden Analiz Elde Etme Earned Nis 18, 2024 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Nis 13, 2024 EDT
Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama Earned Nis 11, 2024 EDT
Engineer Data for Predictive Modeling with BigQuery ML Earned Nis 9, 2024 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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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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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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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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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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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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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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