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Data Engineering with Google Cloud Platform

You're reading from   Data Engineering with Google Cloud Platform A guide to leveling up as a data engineer by building a scalable data platform with Google Cloud

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781835080115
Length 476 pages
Edition 2nd 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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Table of Contents (19) Chapters Close

Preface 1. Part 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. Part 2: Build Solutions with GCP Components
5. Chapter 3: Building a Data Warehouse in BigQuery 6. Chapter 4: Building Workflows 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 to Make Data-Driven Decisions with Looker Studio 10. Chapter 8: Building Machine Learning Solutions on GCP 11. Part 3: Key Strategies for Architecting Top-Notch Solutions
12. Chapter 9: User and Project Management in GCP 13. Chapter 10: Data Governance in GCP 14. Chapter 11: Cost Strategy in GCP 15. Chapter 12: CI/CD on GCP for Data Engineers 16. Chapter 13: Boosting Your Confidence as a Data Engineer 17. Index 18. Other Books You May Enjoy

Exercise – deploying Cloud Composer jobs using Cloud Build

In this section, we will continue creating a Cloud Build pipeline. This time, I will help you get an idea of how this practice can be implemented in terms of data engineering. To do that, we will try to create a CI/CD pipeline to deploy a Cloud Composer DAG.

In this exercise, we will use the DAG from Chapter 4, Building Workflows for Batch Data Loading Using Cloud Composer. Let’s refresh ourselves a little bit on the exercises from that chapter.

In Chapter 4, Building Workflows for Batch Data Loading Using Cloud Composer, we learned how Cloud Composer works. We learned that in Cloud Composer, you can develop DAGs to create data pipelines. These data pipelines can use Airflow operators to manage BigQuery, CloudSQL, GCS, or simple Bash scripts. In those exercises, we practiced five levels of DAGs, with the level-one DAG being the simplest one and the level-five DAG being the most complex. To deploy a DAG,...

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