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

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

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

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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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Google BigQuery: The Definitive Guide (Data Warehousing, Analytics, and Machine Learning at Scale) By Valliappa Lakshmanan, Jordan Tigani. Format: Paperback. ISBN: 9781492044468. List Price: $59.99. Products & Services | Google Cloud
Knative · Shielded VMs · Container Security · Serverless Guide . BigQuery. A fully managed, highly scalable data warehouse with built-in ML. Build and deploy machine learning models on structured data. Cloud Inference API (alpha). Quickly run large-scale correlations over typed time-series datasets. API Analytics. Google BigQuery: The Definitive Guide, Valliappa - Fishpond
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Big Data & Machine Learning This article explains how to use BigQuery as a data warehouse, first Each dataset is tied to a Google Cloud Platform (GCP) project. . Given the speed and scale at which BigQuery operates, many traditional data integrity can require increased machine time and sometimes human time 

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