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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

Summary

In this chapter, we've learned about streaming data and we learned how to handle incoming data as soon as data 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, or a second option is to use a change data capture (CDC) tool. In GCP, you can use a Google provided tool for CDC called Datastream. CDC tools can be attached to the backend database like CloudSQL to publish data changes such as insert, update, and delete operations. We as data engineers are responsible for using Pub/Sub, as we've learned in this chapter.

The second part of streaming data is how to process the data. In this chapter, we've 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. Do keep in mind that you can also handle data from Pub/Sub using Dataflow in batch manner...

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