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Apply your skills in Google Cloud console

Jinou Yang

Member since 2024

Silver League

8810 points
Serverless Data Processing with Dataflow: Foundations Earned أبريل 2, 2025 EDT
Build a Data Warehouse with BigQuery Earned ديسمبر 10, 2024 EST
Introduction to Generative AI Earned ديسمبر 4, 2024 EST
Level 3: BigQuery and Firebase Earned ديسمبر 4, 2024 EST
Build Batch Data Pipelines on Google Cloud Earned نوفمبر 18, 2024 EST
Preparing for your Professional Data Engineer Journey Earned نوفمبر 7, 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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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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This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.

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Start your next Arcade Adventure that will set you on the path to becoming a Professional Cloud Database Engineer. Pick up essential BigQuery and Firebase skills, and earn an exclusive Google Cloud credential along the way!

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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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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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