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ajay arige

Учасник із 2022

Бронзова ліга

Кількість балів: 8995
Serverless Data Processing with Dataflow: Foundations Earned вер. 24, 2024 EDT
Data Warehousing for Partners: Process Data with Dataproc Earned лип. 3, 2024 EDT
Organize and Govern Data with Knowledge Catalog Earned квіт. 25, 2024 EDT
Data Lake Modernization on Google Cloud: Cloud Composer Earned бер. 21, 2024 EDT
Build a Data Warehouse with BigQuery Earned січ. 9, 2024 EST
Google Cloud Big Data and Machine Learning Fundamentals - українська Earned груд. 26, 2023 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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This course explores the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataproc.

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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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Welcome to Cloud Composer, where we discuss how to orchestrate data lake workflows with Cloud Composer.

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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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Під час курсу ви зможете ознайомитися з продуктами й сервісами Google Cloud для роботи з масивами даних і машинним навчанням, які підтримують життєвий цикл роботи з даними для тренування моделей штучного інтелекту. У курсі розглядаються процеси, проблеми й переваги створення конвеєру масиву даних і моделей машинного навчання з Vertex AI у Google Cloud.

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