基于Google云平台的批量数据管道构建

Building Batch Data Pipelines on GCP

529 次查看
Google 云端平台
Coursera
  • 完成时间大约为 15 个小时
  • 中级
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

课程概况

Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud Platform for data transformation including BigQuery, executing Spark on Cloud Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Cloud Dataflow. Learners will get hands-on experience building data pipeline components on Google Cloud Platform using QwikLabs.

课程大纲

Introduction

In this module, we introduce the course and agenda

Introduction to Batch Data Pipelines

This module reviews different methods of data loading: EL, ELT and ETL and when to use what

Executing Spark on Cloud Dataproc

This module shows how to run Hadoop on Cloud Dataproc, how to leverage GCS, and how to optimize your Dataproc jobs.

Manage Data Pipelines with Cloud Data Fusion and Cloud Composer

This module shows how to manage data pipelines with Cloud Data Fusion and Cloud Composer.

Serverless Data Processing with Cloud Dataflow

This module covers using Cloud Dataflow to build your data processing pipelines

Summary

This module reviews the topics covered in this course

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