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Google BigQuery Analytics [Kindle Edition]

Jordan Tigani , Siddartha Naidu
5.0 out of 5 stars  See all reviews (1 customer review)

Print List Price: £30.99
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Book Description

How to effectively use BigQuery, avoid common mistakes, and execute sophisticated queries against large datasets

Google BigQuery Analytics is the perfect guide for business and data analysts who want the latest tips on running complex queries and writing code to communicate with the BigQuery API. The book uses real-world examples to demonstrate current best practices and techniques, and also explains and demonstrates streaming ingestion, transformation via Hadoop in Google Compute engine, AppEngine datastore integration, and using GViz with Tableau to generate charts of query results. In addition to the mechanics of BigQuery, the book also covers the architecture of the underlying Dremel query engine, providing a thorough understanding that leads to better query results.

  • Features a companion website that includes all code and data sets from the book
  • Uses real-world examples to explain everything analysts need to know to effectively use BigQuery
  • Includes web application examples coded in Python

Product Description


“If you’re at all interested in Google BigQuery, this is an excellent book. The descriptions and sample code are clear and easy to understand, and the fact the authors are so involved with the project means they include insights into why things were designed in that particular way. There are useful descriptions of the differences between BigQuery and other tools such as MapReduce, and overall you’ll come out with a much clearer view of the big data scene right now, and how everything fits together.” (I Programmer, August 2014)

From the Back Cover

Run complex queries and write code with BigQuery If youre a data analyst, developer, or data scientist, BigQuery can save you big time, allowing you to run complex queries over billions of recordsand get answers in seconds. In Google BigQuery Analytics, youll learn how to use BigQuery effectively, avoid common pitfalls, and execute sophisticated queries against complex data sets. Explaining the hows and whys of BigQuery, this book gives expert advice to help you get your data in, get it out, and gain the most actionable insights from your analysis. Explains the details of authentication and communication with the BigQuery API Shows you how to get your data in and out of the service Demonstrates how to compose powerful queries from simple pieces Covers using the service from the Google Cloud Platform and from third–party tools Offers real–world examples that demonstrate current best practices and techniques Includes web application examples coded in Python, a popular language for big data analytics Features a companion website and public datasets that are used in the book

Product details

  • Format: Kindle Edition
  • File Size: 7208 KB
  • Print Length: 528 pages
  • Publisher: Wiley; 1 edition (21 May 2014)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: #355,918 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Most Helpful Customer Reviews
5.0 out of 5 stars Very good introduction to BigQuery 11 Sept. 2014
Format:Kindle Edition|Verified Purchase
Very good introduction to BigQuery, the syntax and how it works.

It will probably be slightly out of date in parts - BigQuery is a product that is changing rapidly. I found it most useful to use this text to gain understanding, and then using the online documentation and the Stackoverflow group to get more detail where required.
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Most Helpful Customer Reviews on (beta) 5.0 out of 5 stars  2 reviews
3 of 3 people found the following review helpful
5.0 out of 5 stars Great introduction and explanation to more than just BigQuery 3 Jun. 2014
By J. A. Kowal - Published on
Format:Kindle Edition|Verified Purchase
(Full disclosure: I am a Google employee and have responsibilities related to BigQuery.)

As someone who needed to get up-to-speed on BigQuery in a hurry, this book was exactly what I needed. It's comprehensive and detailed, yet still a very easy read. The authors do a great job of exposing and building knowledge in a very logical order, giving quick explanations/refreshers on related technology along the way.

The book is written in a professional and engaging style that isn't at all dry. Highly recommended for those, like me, who are looking to really understand this product, usage, and context.
2 of 2 people found the following review helpful
5.0 out of 5 stars Excellent guide for both big data analysis with BigQuery and the architecture of big data systems 6 Jun. 2014
By Mike W. - Published on
Format:Kindle Edition|Verified Purchase
This book is easy to read, providing a great description of BigQuery with concrete examples. It also provides throughout the text an overview of multiple big data systems, not just BigQuery, in a very natural and readable manner. Setup and examples are logically structured, starting with the basics such as environment setup and the core API along with a brief introduction to the web interface. Examples are primarily written in Python with some Java sprinkled throughout for completeness. There's clear descriptions of the differences between BigQuery syntax and standard SQL, comprehensive API examples and the current capabilities of the platform.

One of the more interesting and useful pieces is found halfway through the book once the basics have been covered. The authors demonstrate how BigQuery can integrate in to a larger project for recording and analyzing data, using an Android client, Google App Engine and a reporting interface to give a nice example of an end-to-end implementation.

What really sets this book apart however is the descriptions and reasoning given throughout the book on why various architectural decisions were made. It is made very clear how BigQuery differs from other big data technology stacks such as MapReduce, RedShift and others, often delving in to specific design descriptions for comparison. Having this context allows the user to really understand how the system fits together, giving all the tools required to get the most out of BigQuery.
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