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Google Bigquery Analytics/ Jordan Tigani, Siddartha Naidu

By: Tigani, Jordan [author]Contributor(s): Naidu, Siddartha [author]Material type: Text

TextPublication details: Indianapolis: John Wiley & Sons, Inc., 2014 ©2014Description: 508 pages: illustrations; 24 cmISBN: 9781118824825Subject(s): Google Analytics | Google BigQuery | Web usage miningDDC classification: 025.04 TIG 2014 Summary: 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.

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Item type	Current library	Call number	Status	Barcode
BUKU	Pusat Sumber Pendidikan IABI	025.04 TIG 2014 (Browse shelf (Opens below))	Available	0000009309
BUKU	Pusat Sumber Pendidikan IABI	025.04 TIG 2014 (Browse shelf (Opens below))	Available	0000009310
BUKU	Pusat Sumber Pendidikan IABI	025.04 TIG 2014 (Browse shelf (Opens below))	Available	0000009311

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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.

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