Work with Gemini Models in BigQuery
Earned fév. 25, 2026 EST
Boost Productivity with Gemini in BigQuery
Earned fév. 24, 2026 EST
Build Batch Data Pipelines on Google Cloud
Earned fév. 20, 2026 EST
Build Streaming Data Pipelines on Google Cloud
Earned fév. 6, 2026 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned jan. 21, 2026 EST
Introduction to Data Engineering on Google Cloud
Earned mai 6, 2025 EDT
Build a Data Warehouse with BigQuery
Earned avr. 30, 2025 EDT
This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.
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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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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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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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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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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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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.
Work with Gemini Models in BigQuery
Earned fév. 25, 2026 EST
Boost Productivity with Gemini in BigQuery
Earned fév. 24, 2026 EST
Build Batch Data Pipelines on Google Cloud
Earned fév. 20, 2026 EST
Build Streaming Data Pipelines on Google Cloud
Earned fév. 6, 2026 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned jan. 21, 2026 EST
Introduction to Data Engineering on Google Cloud
Earned mai 6, 2025 EDT
Build a Data Warehouse with BigQuery
Earned avr. 30, 2025 EDT