This course explores Gemini in BigQuery, a suite of AI-driven features to assist data-to-AI workflow. These features include data exploration and preparation, code generation and troubleshooting, and workflow discovery and visualization. Through conceptual explanations, a practical use case, and hands-on labs, the course empowers data practitioners to boost their productivity and expedite the development pipeline.
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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 Course is utilized to certify completion of The Skills Challenge at Next 2025.
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Complete the introductory Organize and Govern Data with Knowledge Catalog skill badge to demonstrate skills in the following: creating Knowledge Catalog assets, creating aspect types, and applying aspects to entries in Knowledge Catalog.
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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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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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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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Boost Productivity with Gemini in BigQuery
Earned mar 26, 2025 EDT
Serverless Data Processing with Dataflow: Foundations
Earned mar 24, 2025 EDT
[DEPRECATED] The Skills Challenge at Next 2025
Earned mar 24, 2025 EDT
Organize and Govern Data with Knowledge Catalog
Earned lut 23, 2025 EST
Build Batch Data Pipelines on Google Cloud
Earned lut 12, 2025 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned sty 8, 2025 EST
Build a Certification Study Guide: PDE Exam Prep
Earned lis 12, 2024 EST
Introduction to Data Engineering on Google Cloud
Earned lis 10, 2024 EST