Название: Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale, First Edition
Автор: Valliappa Lakshmanan, Jordan Tigani
Издательство: O’Reilly Media, Inc.
Год: 2019
Страниц: 498
Язык: английский
Формат: epub
Размер: 10.1 MB

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.

In this book, we provide a thorough tour of BigQuery, a serverless, highly scalable, low-cost enterprise data warehouse that is available on Google Cloud. Because there is no infrastructure to manage, enterprises can focus on analyzing data to find meaningful insights using familiar SQL.

Our goal with BigQuery has been to build a data platform that provides leading-edge capabilities, takes advantage of the many great technologies that are now available in cloud environments, and supports tried-and-true data technologies that are still relevant today. For example, on the leading edge, Google’s BigQuery is a serverless compute architecture that decouples compute and storage.

Who Is This Book For?
This book is for data analysts, data engineers, and data scientists who want to use BigQuery to derive insights from large datasets. Data analysts can interact with BigQuery through SQL and via dashboarding tools like Looker, Data Studio, and Tableau.