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Data Engineering with Google Cloud Platform

You're reading from   Data Engineering with Google Cloud Platform A practical guide to operationalizing scalable data analytics systems on GCP

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781800561328
Length 440 pages
Edition 1st Edition
Languages
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Author (1):
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Adi Wijaya Adi Wijaya
Author Profile Icon Adi Wijaya
Adi Wijaya
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Getting Started with Data Engineering with GCP
2. Chapter 1: Fundamentals of Data Engineering FREE CHAPTER 3. Chapter 2: Big Data Capabilities on GCP 4. Section 2: Building Solutions with GCP Components
5. Chapter 3: Building a Data Warehouse in BigQuery 6. Chapter 4: Building Orchestration for Batch Data Loading Using Cloud Composer 7. Chapter 5: Building a Data Lake Using Dataproc 8. Chapter 6: Processing Streaming Data with Pub/Sub and Dataflow 9. Chapter 7: Visualizing Data for Making Data-Driven Decisions with Data Studio 10. Chapter 8: Building Machine Learning Solutions on Google Cloud Platform 11. Section 3: Key Strategies for Architecting Top-Notch Data Pipelines
12. Chapter 9: User and Project Management in GCP 13. Chapter 10: Cost Strategy in GCP 14. Chapter 11: CI/CD on Google Cloud Platform for Data Engineers 15. Chapter 12: Boosting Your Confidence as a Data Engineer 16. Other Books You May Enjoy

Building an ephemeral cluster using Dataproc and Cloud Composer

Another option to manage ephemeral clusters is using Cloud Composer. We learned about Airflow in the previous chapter to orchestrate BigQuery data loading. But as we've already learned, Airflow has many operators and one of them is of course Dataproc. 

You should use this approach compared to a workflow template if your jobs are complex, in terms of developing a pipeline that contains many branches, backfilling logic, and dependencies to other services, since workflow templates can't handle these complexities.

In this section, we will use Airflow to create a Dataproc cluster, submit a pyspark job, and delete the cluster when finished.

Check the full code in the GitHub repository:

Link to be updated

To use the Dataproc operators in Airflow, we need to import the operators, like this:

from airflow.providers.google.cloud.operators.dataproc import (
    DataprocCreateClusterOperator...
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