Google BigQuery Analytics.

Format:

Book

Author/Creator:

Tigani, Jordan.

Contributor:

Naidu, Siddartha.

Language:

English

Subjects (All):

Google analytics.

Google BigQuery.

Web usage mining.

Google.

Local Subjects:

Google analytics.

Google BigQuery.

Web usage mining.

Physical Description:

1 online resource (530 p.)

Edition:

1st ed.

Place of Publication:

Hoboken : Wiley, 2014.

Language Note:

English

Summary:

How to effectively use BigQuery, avoid common mistakes, and execute sophisticated queries against large datasets Google BigQuery Analytics 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 r

Contents:

Cover; Title Page; Copyright; Contents; Part I BigQuery Fundamentals; 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; Cloud Processing; Cloud Storage; Cloud Analytics; Problem Statement; What Is Big Data?; Why Big Data?; Why Do You Need New Ways to Process Big Data?; How Can You Read a Terabyte in a Second?; What about MapReduce?; How Can You Ask Questions of Your Big Data and Quickly Get Answers?; Summary; Chapter 2 BigQuery Fundamentals; What Is BigQuery?; SQL Queries over Big Data

Cloud Storage SystemDistributed Cloud Computing; Analytics as a Service (AaaS?); What BigQuery Isn't; BigQuery Technology Stack; Google Cloud Platform; BigQuery Service History; BigQuery Sensors Application; Sensor Client Android App; BigQuery Sensors AppEngine App; Running Ad-Hoc Queries; Summary; Chapter 3 Getting Started with BigQuery; Creating a Project; Google APIs Console; Free Tier Limitations and Billing; Running Your First Query; Loading Data; Using the Command-Line Client; Install and Setup; Using the Client; Service Account Access; Setting Up Google Cloud Storage

Development EnvironmentPython Libraries; Java Libraries; Additional Tools; Summary; Chapter 4 Understanding the BigQuery Object Model; Projects; Project Names; Project Billing; Project Access Control; Projects and AppEngine; BigQuery Data; Naming in BigQuery; Schemas; Tables; Datasets; Jobs; Job Components; BigQuery Billing and Quotas; Storage Costs; Processing Costs; Query RPCs; TableData.insertAll() RPCs; Data Model for End-to-End Application; Project; Datasets; Tables; Summary; Part II Basic BigQuery; Chapter 5 Talking to the BigQuery API; Introduction to Google APIs

Authenticating API AccessRESTful Web Services for the SOAP-Less Masses; Discovering Google APIs; Common Operations; BigQuery REST Collections; Projects; Datasets; Tables; TableData; Jobs; BigQuery API Tour; Error Handling in BigQuery; Summary; Chapter 6 Loading Data; Bulk Loads; Moving Bytes; Destination Table; Data Formats; Errors; Limits and Quotas; Streaming Inserts; Summary; Chapter 7 Running Queries; BigQuery Query API; Query API Methods; Query API Features; Query Billing and Quotas; BigQuery Query Language; BigQuery SQL in Five Queries; Differences from Standard SQL; Summary

Chapter 8 Putting It TogetherA Quick Tour; Mobile Client; Monitoring Service; Log Collection Service; Log Trampoline; Dashboard; Data Caching; Data Transformation; Web Client; Summary; Part III Advanced BigQuery; Chapter 9 Understanding Query Execution; Background; Storage Architecture; Colossus File System (CFS); ColumnIO; Durability and Availability; Query Processing; Dremel Serving Trees; Architecture Comparisons; Relational Databases; MapReduce; Summary; Chapter 10 Advanced Queries; Advanced SQL; Subqueries; Combining Tables: Implicit UNION and JOIN; Analytic and Windowing Functions

BigQuery SQL Extensions

Notes:

Description based upon print version of record.

ISBN:

1-118-82487-3