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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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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

How to create materialized views and understanding how BI Engine works

BigQuery has a feature called materialized views. It's not a table, nor a view; it's a materialized view. To understand it, let's go back to what a table is compared to a view. One of the reasons you create tables is that you want to store transformation results to be used for downstream usage. The reason you create a view instead of a table is that you need the data in real time, but with a view, you always pre-compute all the processes. A materialized view is somewhere in between. With materialized views, you can have real-time access, but the processes aren't pre-computed. 

It's easier to understand is by trying it in practice, so let's set up a scenario. Let's use our facts_trip_daily table and run this query from the BigQuery console:

SELECT trip_date, sum(sum_duration_sec) as sum_duration_sec 
FROM `packt-data-eng-on-gcp.dwh_bikesharing.facts_trips_daily`...
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