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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

Exercise: Creating and running jobs on a Dataproc cluster

In this exercise, we will try two different methods to submit a Dataproc job. In the previous exercise, we used the Spark shell to run our Spark syntax, which is common when practicing but not common in real development. Usually, we would only use the Spark shell for initial checking or testing simple things. In this exercise, we will code Spark jobs in editors and submit them as jobs. 

Here are the scenarios that we want to try:

  • Preparing log data in GCS and HDFS
  • Developing Spark ETL from HDFS to HDFS
  • Developing Spark ETL from GCS to GCS
  • Developing Spark ETL from GCS to BigQuery

Let's look at each of these scenarios in detail.

Preparing log data in GCS and HDFS

The log data is in our GitHub repository, located here:

https://github.com/PacktPublishing/Data-Engineering-with-Google-Cloud-Platform/tree/main/chapter-5/dataset/logs_example

If you haven't cloned the repository...

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