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Big Data, Cloud, Semantic Web, and Social Network Technologies
Published in Bhavani Thuraisngham, Murat Kantarcioglu, Latifur Khan, Secure Data Science, 2022
Bhavani Thuraisngham, Murat Kantarcioglu, Latifur Khan
BigQuery is essentially a data warehouse that manages petabyte scale data. It runs on Google's infrastructure and can process SQL queries or carry out analytics extremely fast. For example, terabyte data can be accessed in seconds while petabyte data can be accessed in minutes. The BigQuery data is stored in different types of tables: native tables store the BigQuery data, views stores the virtual tables, and external tables store the external data. BigQuery can be accessed in many ways such as command line tools, RESTful interface or a web user interface, and client libraries (e.g., Java, .NET, Python). More details on BigQuery can be found at [BIGQ].
Epilogue: Towards “big data”
Published in Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, Texts in Statistical Science, 2017
Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton
BigQuery is a Web service offered by Google. Internally, the BigQuery service is supported by Dremel, the open-source version of which is Apache Drill. The bigrquery package for R provides access to BigQuery from within R.
Explaining Digital Technology: Digital Artifact Delineation and Coalescence
Published in Journal of Computer Information Systems, 2023
GO-JEK cloud architecture in GCP is shown in Figure 9a. The system is designed using serverless services, which are fully managed by the cloud service provider. These serverless services auto scale to the growing demands of the business. The serverless deployment of GO-JEK system ingests data from different data sources, processes and enhances data by combining it with additional data sources, stores and analyzes data using SQL. The GCP implementation of the system uses Google Pub/Sub—an ingestion serverless service, Google DataFlow—a data processing/ETL serverless service, and Google BigQuery—a data warehouse serverless service for data storage and querying. Detailed architectural diagrams of these components are available in the appendix section.
Clinical Notes Mining for Post Discharge Mortality Prediction
Published in IETE Technical Review, 2022
Vineet Kumar, Rohit Bajpai, Ram Babu Roy
We used Google BigQuery and its SQL dialect for our patient cohort selection. Clinical notes in MIMIC-III are present in the NOTEEVENTS table. The mean number of notes per hospital admission is 32 (median: 15). We concatenated all notes of a particular patient with the same hospital admission id. One such concatenated sample note is shown in Figure 2. The colour codes are representative and shown for understanding of theuser.
Big Data Analytics Services for Enhancing Business Intelligence
Published in Journal of Computer Information Systems, 2018
Zhaohao Sun, Lizhe Sun, Kenneth Strang
DW + DM + SM + ML+ Visualization+ Optimization are above Big data and data analytics, where DW, DM, SM, and ML are abbreviations of data warehouse, data mining, statistical modeling, and machine learning, respectively [7]. The current leading DW includes Amazon’s Redshift, Google’s BigQuery, Microsoft’s Azure SQL Data Warehouse, and Teradata [3].