Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale

Lakshmanan, Valliappa; Tigani, Jordan

ISBN 10: 1492044466 / ISBN 13: 9781492044468

Published by O'Reilly Media, 2019

About this Item

Seller Inventory # I-9781492044468

Bibliographic Details

• Title: Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale
• Publisher: O'Reilly Media
• Publication Date: 2019
• Language: English
• ISBN 10: 1492044466
• ISBN 13: 9781492044468
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Work with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets. BigQuery enables enterprises to efficiently store, query, ingest, and learn from their data in a convenient framework. With this book, you’ll examine how to analyze data at scale to derive insights from large datasets efficiently.

Valliappa Lakshmanan, tech lead for Google Cloud Platform, and Jordan Tigani, engineering director for the BigQuery team, provide best practices for modern data warehousing within an autoscaled, serverless public cloud. Whether you want to explore parts of BigQuery you’re not familiar with or prefer to focus on specific tasks, this reference is indispensable.

About the Authors

Valliappa (Lak) Lakshmanan is a Tech Lead for Big Data and Machine Learning Professional Services on Google Cloud Platform. His mission is to democratize machine learning so that it can be done by anyone anywhere using Google's amazing infrastructure (i.e., without deep knowledge of statistics or programming or ownership of lots of hardware).

Jordan is engineering director for the core BigQuery team. He was one of the founding engineers on BigQuery, and helped grow it to be one of the most successful products in Google’s Cloud Platform. He wrote the first book on BigQuery, and has also spoken widely on the subject. Jordan has twenty years of software development experience, ranging from Microsoft Research to Machine Learning startups.