Free Downloads Google BigQuery Analytics
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
File Size: 7208 KB
Print Length: 492 pages
Page Numbers Source ISBN: 1118824822
Publisher: Wiley; 1 edition (May 21, 2014)
Publication Date: May 21, 2014
Sold by: Digital Services LLC
Language: English
ASIN: B00JUUZIZI
Text-to-Speech: Enabled
X-Ray: Not Enabled
Word Wise: Not Enabled
Lending: Not Enabled
Enhanced Typesetting: Not Enabled
Best Sellers Rank: #251,740 Paid in Kindle Store (See Top 100 Paid in Kindle Store) #39 in Books > Computers & Technology > Programming > Algorithms > Data Structures #94 in Books > Computers & Technology > Databases & Big Data > Data Warehousing #96 in Books > Computers & Technology > Networking & Cloud Computing > Network Administration > Storage & Retrieval
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.
(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.
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