Building Batch Data Pipelines on Google Cloud

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Course Overview

Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) 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 get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.

Who Should Attend

Developers responsible for designing pipelines and architectures for data processing.

Course Objectives

    • Review different methods of data loading: EL, ELT and ETL and when to use what
    • Run Hadoop on Dataproc, leverage Cloud Storage, and optimize Dataproc jobs
    • Build your data processing pipelines using Dataflow
    • Manage data pipelines with Data Fusion and Cloud Composer

Course Outline

  • Introduction
  • Introduction to Building Batch Data Pipelines
  • Executing Spark on Dataproc
  • Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
  • Course Summary

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Class Dates & Times

Class times are listed Central time

This is a 1-day class

Class dates not listed.
Please contact us for available dates and times.