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Learning Google BigQuery

You're reading from   Learning Google BigQuery A beginner's guide to mining massive datasets through interactive analysis

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781787288591
Length 264 pages
Edition 1st Edition
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Authors (3):
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Eric Brown Eric Brown
Author Profile Icon Eric Brown
Eric Brown
LinkedIn
Eric Brown currently works as an analytics manager for PMG advertising in Austin, Texas. Eric has over 11 years of experience in the data analytics field. He has been working on the Google Cloud Platform for over 3 years. He oversees client web analytics implementations and implements big data integrations in both Google BigQuery and Amazon Redshift. Eric has a passion for analytics, and especially for visualization and data manipulation through open source tools such as R. He has worked in various roles in various verticals, such as web analytics service providers, media companies, real-estate online marketing, and advertising.
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Thirukkumaran Haridass Thirukkumaran Haridass
Author Profile Icon Thirukkumaran Haridass
Thirukkumaran Haridass
Thirukkumaran Haridass currently works as a lead software engineer at Builder Homesite Inc. in Austin, Texas, USA. He has over 15 years of experience in the IT industry. He has been working on the Google Cloud Platform for more than 3 years. Haridass is responsible for the big data initiatives in his organization that help the company and its customers realize the value of their data. He has played various roles in the IT industry and worked for Fortune 500 companies in various verticals, such as retail, e-commerce, banking, automotive, and presently, real estate online marketing.
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Mikhail Berlyant Mikhail Berlyant
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Mikhail Berlyant
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Table of Contents (9) Chapters Close

Preface 1. Google Cloud and Google BigQuery 2. Google Cloud SDK FREE CHAPTER 3. Google BigQuery Data Types 4. BigQuery SQL Basic 5. BigQuery SQL Advanced 6. Google BigQuery API 7. Visualizing BigQuery Data 8. Google Cloud Pub/Sub

Summary

This chapter explained how to use the Cloud Pub/Sub messaging system for your projects to help facilitate communication between various applications asynchronously. Messages inserted by one application can be read by another application and stored in a persistent data storage, such as Google Cloud Storage or BigQuery. Cloud Dataprep is product that helps you load data from your local data source to the cloud and transform it for loading into BigQuery or Google Cloud Storage. I hope this chapter helped you implement real-time analytics and reporting using Cloud Pub/Sub and other services from Google Cloud Platform!

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Authors (3)

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Profile icon Haridass
Haridass
Thirukkumaran Haridass currently works as a lead software engineer at Builder Homesite Inc. in Austin, Texas, USA. He has over 15 years of experience in the IT industry. He has been working on the Google Cloud Platform for more than 3 years. Haridass is responsible for the big data initiatives in his organization that help the company and its customers realize the value of their data. He has played various roles in the IT industry and worked for Fortune 500 companies in various verticals, such as retail, e-commerce, banking, automotive, and presently, real estate online marketing.
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https://github.com/outoftime
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