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Data Engineering with Google Cloud Platform - Second Edition

You're reading from  Data Engineering with Google Cloud Platform - Second Edition

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781835080115
Pages 476 pages
Edition 2nd Edition
Languages
Author (1):
Adi Wijaya Adi Wijaya
Profile icon Adi Wijaya

Table of Contents (19) Chapters

Preface 1. Part 1: Getting Started with Data Engineering with GCP
2. Chapter 1: Fundamentals of Data Engineering 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

Introduction to CDC and Datastream

Now that we’ve learned about Pub/Sub Dataflow streaming, let’s get a better idea of how the data starts being pushed from the source system to BigQuery. Unfortunately, in the real world, there are many cases in which you can’t change the source system code at all. This means that you can’t add a Pub/Sub publisher to publish the records for streaming.

This may happen for many reasons – for example, in an organization such as banking. The core application is usually a monolith product that is developed by third-party vendors. Even if it’s developed internally, the complexity of the banking core system makes it difficult to change the code to add a Pub/Sub publisher in every data point. How can we solve this?

Back to our learning batch pipeline, we must extract data from tables in databases. We export the database’s table into files and load it to BigQuery. Can we do the same for streaming?

Unfortunately...

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