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Google BigQuery Analytics /

This 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 ing...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Tigani, Jordan, Naidu, Siddartha (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Indianpoolis : John Wiley & Sons, Inc., [2014?]
Ã2014
Temas:
Acceso en línea:Texto completo
Texto completo

MARC

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245 1 0 |a Google BigQuery Analytics /  |c Jordan Tigani, Siddartha Naidu. 
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500 |a Includes index. 
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520 |a This 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. It 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. --  |c Edited summary from book. 
504 |a Includes bibliographical references and index. 
505 0 |a Part I: BigQuery Fundamentals; In This Part; Chapter 1: The Story of Big Data at Google; Big Data Stack 1.0; Big Data Stack 2.0 (and Beyond); Open Source Stack; Google Cloud Platform; Problem Statement; Summary; Chapter 2: BigQuery Fundamentals; What Is BigQuery?; BigQuery Sensors Application; Summary; Chapter 3: Getting Started with BigQuery; Creating a Project; Running Your First Query; Using the Command-Line Client; Setting Up Google Cloud Storage; Development Environment; Summary; Chapter 4: Understanding the BigQuery Object Model; Projects; BigQuery Data; Jobs. 
505 8 |a BigQuery Billing and QuotasData Model for End-to-End Application; Summary; Part II: Basic BigQuery; In This Part; Chapter 5: Talking to the BigQuery API; Introduction to Google APIs; BigQuery REST Collections; Summary; Chapter 6: Loading Data; Bulk Loads; Streaming Inserts; Summary; Chapter 7: Running Queries; BigQuery Query API; BigQuery Query Language; Summary; Chapter 8: Putting It Together; A Quick Tour; Mobile Client; Log Collection Service; Dashboard; Summary; Part III: Advanced BigQuery; In This Part; Chapter 9: Understanding Query Execution; Background; Storage Architecture. 
505 8 |a Query ProcessingArchitecture Comparisons; Summary; Chapter 10: Advanced Queries; Advanced SQL; BigQuery SQL Extensions; Query Errors; Recipes; Summary; Chapter 11: Managing Data Stored in BigQuery; Query Caching; Result Caching; Table Snapshots; AppEngine Datastore Integration; Metatables and Table Sharding; Summary; Part IV: BigQuery Applications; In This Part; Chapter 12: External Data Processing; Getting Data Out of BigQuery; AppEngine MapReduce; Querying BigQuery from a Spreadsheet; Summary; Chapter 13: Using BigQuery from Third-Party Tools; BigQuery Adapters. 
505 8 |a Scientific Data Processing Tools in BigQueryVisualizing Data in BigQuery; Summary; Chapter 14: Querying Google Data Sources; Google Analytics; Google AdSense; Google Cloud Storage; Summary; Introduction; Overview of the Book and Technology; How This Book Is Organized; How to Read This Book; Tools You Need; Supplemental Materials and Information; End User License Agreement. 
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