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Building Batch Data Pipelines on Google Cloud - Locales

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Overview

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This course, Building Batch Data Pipelines on Google Cloud - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Building Batch Data Pipelines on Google Cloud.

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 for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners will get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.

Syllabus

Introduction

In this module, we introduce the course and agenda

Introduction to Building Batch Data Pipelines

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

Executing Spark on Dataproc

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

Serverless Data Processing with Dataflow

This module covers using Dataflow to build your data processing pipelines

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.

Course Summary

Course Summary

Course Resources

PDF links to all modules

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