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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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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

Creating 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 by trying it in practice, so let’s set up a scenario:

  1. Create an aggregation query in the BigQuery console.

    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...
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