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Francisco Javier Contreras

Menjadi anggota sejak 2022

Serverless Data Processing with Dataflow: Foundations Earned Nov 26, 2024 EST
Build Streaming Data Pipelines on Google Cloud Earned Nov 24, 2024 EST
Bekerja dengan Model Gemini di BigQuery Earned Nov 18, 2024 EST
Build Data Lakes and Data Warehouses on Google Cloud Earned Okt 23, 2024 EDT
Build a Certification Study Guide: PDE Exam Prep Earned Jan 10, 2024 EST
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned Agu 30, 2023 EDT
Vertex AI Earned Mei 24, 2022 EDT

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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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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Kursus ini menunjukkan cara menggunakan model AI/ML untuk tugas-tugas AI generatif di BigQuery. Melalui kasus penggunaan praktis yang melibatkan pengelolaan hubungan pelanggan (CRM), Anda akan mempelajari alur kerja pemecahan masalah bisnis dengan model Gemini. Untuk memudahkan pemahaman, kursus ini juga menyediakan panduan langkah demi langkah melalui solusi coding menggunakan kueri SQL dan notebook Python.

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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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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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Welcome Gamers! Learn Google Cloud's Vertex AI, all while having fun! Vertex AI is Google Cloud's unified ML platform for solving your tough business problems. You will compete to see who can finish the game with the highest score. Earn the points by completing the steps in the lab.... and get bonus points for speed! Be sure to click "End" when you're done with each lab to get the maximum points. All players will be awarded the game badge.

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