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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 FREE CHAPTER
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

Processing streaming data

In the big data era, people like to correlate big data with real-time data. Some people say that if the data is not real time, then it’s not big data. This statement is partially true. In reality, the majority of data pipelines in the world use the batch approach, and that’s why it’s still very important for data engineers to understand the batch data pipeline. From Chapter 3, Building a Data Warehouse on BigQuery, to Chapter 5, Building a Data Lake Using Dataproc, we focused on handling batch data pipelines.

However, real-time capabilities in the big data era are something that many data engineers need to start to rethink in terms of data architecture. To understand more about architecture, we need to have a clear definition of what real-time data is.

From the end user perspective, real-time data can mean anything – from faster access to data, more frequent data refresh, and detecting events as soon as they happen. From a...

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