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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/cloud-data-engineering-duke
课程评论：没有评论
课程名称：云数据工程 课程概述：欢迎来到“云计算解决方案规模构建”专项课程的第三门课程！在本课程中，您将学习如何将数据工程应用于实际项目，利用前两门课程中介绍的云计算概念。课程结束时，您将能够开发数据工程应用程序，并使用软件开发最佳实践来创建数据工程应用，包括持续部署、代码质量工具、日志记录、仪器监控等。最终，您将使用云原生技术来解决复杂的数据工程解决方案。 本课程非常适合初学者以及对将云计算应用于数据科学、机器学习和数据工程感兴趣的中级学生。学生需要具备初级Linux和中级Python技能。在本课程的项目中，您将建立一个无服务器的数据工程管道，云平台可以选择Amazon Web Services (AWS)、Azure或Google Cloud Platform (GCP)。 课程大纲： 1. **云数据工程入门**：您将学习数据工程的方法论，并评估面对摩尔定律终结的最佳实践，开发适用软件工程最佳实践的分布式系统，并评估实施大数据解决方案的最佳实践。您将应用这些实践，构建一个使用Numba和CUDA SDK的GPU编程项目。 2. **数据工程原则探讨**：您将学习数据工程的定义，以及如何在数据工程中应用软件工程的最佳实践。接着，您将通过构建命令行数据处理工具来应用这些知识。 3. **构建数据工程管道**：您将学习无服务器数据工程技术和数据治理最佳实践，然后通过构建一个无服务器数据工程系统来应用这些知识。 4. **关键数据工程任务的应用**：您将学习关键的数据工程任务，包括ETL、云数据库和云存储，并通过构建一个使用AWS Rekognition API给图像打标签的无服务器AWS lambda函数来应用这些知识。
Name:Getting Started with Cloud Data Engineering
Description:This week, you will learn about the methodologies involved in Data Engineering. You will also learn to evaluate best practices for dealing with the end of Moore’s Law, develop distributed systems that apply software engineering best practices and evaluate best practices for implementing solutions with Big Data. You will apply these practices to build a GPU programming project using Numba and the CUDA SDK.
Name:Examining Principles of Data Engineering
Description:This week, you will learn what Data Engineering is and how to use software engineering best practices in Data Engineering. You will then apply this knowledge by building a command-line data processing tool.
Name:Building Data Engineering Pipelines
Description:This week, you will learn serverless data engineering techniques and data governance best practices. You will then apply this knowledge by building a serverless Data Engineering system.
Name:Applying Key Data Engineering Tasks
Description:This week, you will learn about key Data Engineering tasks including ETL, Cloud Databases and Cloud Storage. You will then apply this knowledge by building a serverless AWS lambda function that labels an image using the AWS Rekognition API.
Welcome to the third course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn how to apply Data Engineering to real-world projects using the Cloud computing concepts introduced in the first two courses of this series. By the end of this course, you will be able to develop Data Engineering applications and use software development best practices to create data engineering applications. These will include continuous deployment, code quality tools, logging, instrumentation and monitoring. Finally, you will use Cloud-native technologies to tackle complex data engineering solutions. This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you will build a serverless data engineering pipeline in a Cloud platform: Amazon Web Services (AWS), Azure or Google Cloud Platform (GCP).
