Learning Google BigQuery

Packt Publishing Limited
  • erschienen am 22. Dezember 2017
  • Buch
  • |
  • Softcover
  • |
  • 264 Seiten
978-1-78728-859-1 (ISBN)
Get a fundamental understanding of how Google BigQuery works by analyzing and querying large datasets

About This Book
* Get started with BigQuery API and write custom applications using it
* Learn how BigQuery API can be used for storing, managing, and query massive datasets with ease
* A practical guide with examples and use-cases to teach you everything you need to know about Google BigQuery

Who This Book Is For
If you are a developer, data analyst, or a data scientist looking to run complex queries over thousands of records in seconds, this book will help you. No prior experience of working with BigQuery is assumed.

What You Will Learn
* Get a hands-on introduction to Google Cloud Platform and its services
* Understand the different data types supported by Google BigQuery
* Migrate your enterprise data to BigQuery and query it using the legacy and standard SQL techniques
* Use partition tables in your project and query external data sources and wild card tables
* Create tables and data sets dynamically using the BigQuery API
* Perform real-time inserting of records for analytics using Python and C#
* Visualize your BigQuery data by connecting it to third party tools such as Tableau and R
* Master the Google Cloud Pub/Sub for implementing real-time reporting and analytics of your Big Data

In Detail
Google BigQuery is a popular cloud data warehouse for large-scale data analytics. This book will serve as a comprehensive guide to mastering BigQuery, and how you can utilize it to quickly and efficiently get useful insights from your Big Data.
You will begin with getting a quick overview of the Google Cloud Platform and the various services it supports. Then, you will be introduced to the Google BigQuery API and how it fits within in the framework of GCP. The book covers useful techniques to migrate your existing data from your enterprise to Google BigQuery, as well as readying and optimizing it for analysis. You will perform basic as well as advanced data querying using BigQuery, and connect the results to various third party tools for reporting and visualization purposes such as R and Tableau. If you're looking to implement real-time reporting of your streaming data running in your enterprise, this book will also help you.
This book also provides tips, best practices and mistakes to avoid while working with Google BigQuery and services that interact with it. By the time you're done with it, you will have set a solid foundation in working with BigQuery to solve even the trickiest of data problems.

Style and Approach
This book follows a step-by-step approach to teach readers the concepts of Google BigQuery using SQL. To explain various data querying processes, large-scale datasets are used wherever required.
  • Englisch
  • Birmingham
  • |
  • Großbritannien
  • Höhe: 233 mm
  • |
  • Breite: 191 mm
  • |
  • Dicke: 17 mm
  • 462 gr
978-1-78728-859-1 (9781787288591)
1787288595 (1787288595)
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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. 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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