Remove 2006 Remove Business Intelligence Remove Structured Data
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The Good and the Bad of Apache Spark Big Data Processing

AltexSoft

Spark SQL brings native support for SQL to Spark and streamlines the process of querying semistructured and structured data. It incorporates a comprehensive set of libraries, including Spark SQL for structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

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Big Data Analytics: How It Works, Tools, and Real-Life Applications

AltexSoft

A single car connected to the Internet with a telematics device plugged in generates and transmits 25 gigabytes of data hourly at a near-constant velocity. And most of this data has to be handled in real-time or near real-time. Variety is the vector showing the diversity of Big Data. Data storage and processing.

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Google BigQuery: A Game-Changing Data Warehousing Solution

ProjectPro

2) Geospatial Analysis Users can analyze and display geographic data with BigQuery thanks to its usage of geography data types and Google Standard SQL geography functions. Google BigQuery Architecture- A Detailed Overview BigQuery is built on Dremel technology, which has been used internally at Google since 2006.

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The Good and the Bad of Hadoop Big Data Framework

AltexSoft

Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics. According to the study by the Business Application Research Center (BARC), Hadoop found intensive use as. a suitable technology to implement data lake architecture.

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