PDF [DOWNLOAD] Google BigQuery: The Definitive

Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale. Valliappa Lakshmanan, Jordan Tigani

Google-BigQuery-The-Definitive.pdf
ISBN: 9781492044468 | 470 pages | 12 Mb

• Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale
• Valliappa Lakshmanan, Jordan Tigani
• Page: 470
• Format: pdf, ePub, fb2, mobi
• ISBN: 9781492044468
• Publisher: O'Reilly Media, Incorporated

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

Ebooks free downloads txt Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale (English Edition) by Valliappa Lakshmanan, Jordan Tigani 9781492044468

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.

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This practical book is the canonical reference to Google BigQuery, the query engine that lets Data Warehousing, Analytics, and Machine Learning at Scale.
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Selection from Google BigQuery: The Definitive Guide [Book] With this book, you'll examine how to analyze data at scale to derive insights from large datasets efficiently. director for the BigQuery team, provide best practices in modern data warehousing within an autoscaled, Custom Machine Learning Models on GCP.
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