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AWS: Data Analytics

包含在 Coursera Plus

深入了解一个主题并学习基础知识。
中级 等级

推荐体验

6 小时 完成
灵活的计划
自行安排学习进度
深入了解一个主题并学习基础知识。
中级 等级

推荐体验

6 小时 完成
灵活的计划
自行安排学习进度

您将学到什么

  • Explore data integration services to integrate data from multiple sources for analytics and application development.

  • Manage data lake access permissions and share data within and outside your organization.

  • Describe a fully managed service to process and analyze streaming data at any scale in AWS.

要了解的详细信息

可分享的证书

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作业

6 项作业

授课语言:英语(English)

了解顶级公司的员工如何掌握热门技能

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Exam Prep (DEA-C01): AWS Certified Data Engineer - Associate 专项课程 专项课程的一部分
在注册此课程时,您还会同时注册此专项课程。
  • 向行业专家学习新概念
  • 获得对主题或工具的基础理解
  • 通过实践项目培养工作相关技能
  • 获得可共享的职业证书

该课程共有3个模块

Welcome to Week 1 of the AWS: Data Analytics course. This week, you will be introduced to AWS Glue, a fully managed ETL service for customers to prepare and load their data for analytics. You will explore some basic components of AWS Glue such as AWS Glue Catalog, Crawlers, Classifiers, etc. By the end of the week, you will learn some advanced features of AWS Glue Data Quality and AWS Glue DataBrew.

涵盖的内容

9个视频2篇阅读材料2个作业1个讨论话题

Welcome to Week 2 of the AWS: Data Analytics course. This week, you will be introduced to Amazon Athena, an interactive analytics service built on open-source frameworks, that provides a simplified way to analyze petabytes of data where it lives. You will also learn Amazon EMR, a managed cluster platform that simplifies running big data frameworks, such as Apache Hadoop and Apache Spark. By the end of the week, you will perform data integration using Amazon EMR and AWS Glue.

涵盖的内容

9个视频1篇阅读材料2个作业1个非评分实验室

Welcome to Week 3 of the AWS: Data Analytics course. This week, you will be introduced to Data Analytics and ML services in AWS. You will learn Amazon Kinesis, a fully managed service to process and analyze streaming data at scale. You will explore Amazon Managed Service for Apache Flink to transform and analyze streaming data in real-time using Apache Flink. With Amazon QuickSight, one can enhance data-driven organizations with unified business intelligence (BI) at scale. By the end of this week, you will learn Amazon SageMaker, a fully managed service that can help to build, train, and deploy ML models at scale using a single integrated development environment (IDE).

涵盖的内容

5个视频3篇阅读材料2个作业

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位教师

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Whizlabs
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