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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

Introduction to Google Cloud Storage and BigQuery

Google Cloud Storage (GCS) is object storage. It's a service that is fully managed by GCP, which means we don't need to think about any underlying infrastructure for GCS. For example, we don't need to think about pre-sizing the storage, the network bandwidth, number of nodes, or any other infrastructure-related stuff.

What is object storage? Object storage is a highly scalable data storage architecture that can store very large amounts of data in any format. 

Because the technology can store data in almost any size and format, GCS is often used by developers to store any large files, for example, images, videos, and large CSV data. But, from the data engineering perspective, we will often use GCS for storing files, for example, as dump storage from databases, for exporting historical data from BigQuery, for storing machine learning model files, and for any other purpose related to storing files. 

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