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An Inside Look at Google BigQuery

Table of ContentsAbstract ..2 How Google Handles Big Data Daily Operations ..2 BigQuery : Externalization of Dremel ..2 Dremel Can Scan 35 Billion Rows Without an ..3 Index in Tens of Seconds Columnar Storage and Tree architecture of Dremel ..3 Columnar Storage ..4 Tree architecture ..4 Dremel: Key to Run Business at Google Speed ..5 And what is BigQuery ? ..5 BigQuery versus MapReduce ..6 Comparing BigQuery and MapReduce ..6 MapReduce Limitations ..7 BigQuery and MapReduce Comparison ..8 Data Warehouse Solutions and Appliances for OLAP/BI ..10 Relational OLAP (ROLAP) ..10 Multidimensional OLAP (MOLAP) ..10 Full-scan Speed Is the Solution..10 BigQuery s Unique Abilities ..11 Cloud-Powered Massively Parallel Query Service ..11 Why Use the Google Cloud Platform? ..12 Conclusion ..12 References ..12 Acknowledgements ..12An Inside Look at Google BigQueryWhite Paper | BigQueryAn Inside Look at Google BigQueryby Kazunori Sato, Solutions Architect, Cloud Solutions team AbstractThis white paper introduces Google BigQuery , a fully-managed and cloud-based interactive query service for massive datasets.

analytics system that harnesses the computing power of many thousands of servers and is delivered as a cloud service. Tree Architecture One of the challenges Google had in designing Dremel was how to dispatch queries and collect results across tens of thousands of machines in a matter of seconds.

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