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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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Toc

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

Summary

In this chapter, we learned about streaming data and how to handle incoming data as soon as it is created. Data is created using the Pub/Sub publisher client. In practice, you can use this approach by requesting the application developer to send messages to Pub/Sub as the data source, though a second option is to use a CDC tool. In GCP, you can use the Google-provided tool for CDC called Datastream. CDC tools can be attached to the backend database such as CloudSQL to publish data changes such as insert, update, and delete operations.

The second part of streaming data is how to process the data. In this chapter, we learned how to use Dataflow to handle continuously incoming data from Pub/Sub to aggregate it on the fly and store it in BigQuery tables. Keep in mind that you can also handle data from Pub/Sub using Dataflow in a batch manner.

With experience in creating streaming data pipelines on GCP, you will realize how easy it is to start creating one from an infrastructure...

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