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Santhosh Muthu

Member since 2023

Bronze League

17570 points
Boost Productivity with Gemini in BigQuery Earned янв. 21, 2025 EST
Derive Insights from BigQuery Data Earned окт. 19, 2024 EDT
Build a Data Warehouse with BigQuery Earned окт. 18, 2024 EDT
Implement Cloud Storage and Data Protection Solutions Earned окт. 17, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned янв. 17, 2024 EST
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned янв. 8, 2024 EST
Build Batch Data Pipelines on Google Cloud Earned дек. 20, 2023 EST
Build a Certification Study Guide: PDE Exam Prep Earned дек. 20, 2023 EST

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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Complete the introductory Derive Insights from BigQuery Data skill badge course to demonstrate skills in the following: Write SQL queries.Query public tables.Load sample data into BigQuery.Troubleshoot common syntax errors with the query validator in BigQuery.Create reports in Data Studio by connecting to BigQuery data.

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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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Earn a Skill Badge by completing the Implement Cloud Storage and Data Protection Solutions course. Learn to create a Cloud Storage bucket, use the command line, and protect objects with Bucket Lock.

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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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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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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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