The more significant the volume of information, the more time it will take to analyze it. Big Data technology involves the use of specialized programs that operate on the MapReduce principle.
The application selects fragments according to specified criteria, and then the data is distributed between different objects (servers, computers, etc.). Then all these segments are directly processed, and the process develops in parallel.
Data Processing in Big Data
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Let's give some examples:
Hadoop . It is a project of the ghana email list Apache Software Foundation and is a set of open-source utilities. The tool can be used by several specialists simultaneously.
Apache Spark. A collection of libraries and frameworks for processing streaming data at high speed. Applicable for training neural networks.
DWH analysts work with these tools.
Data Analysis
Before using big data arrays in practice, it is necessary to analyze them according to various objective criteria. For this purpose, the following are used:
SQL . A language for generating queries when interacting with a DBMS.
Neural networks . Trainable mathematical models that allow interaction with gigantic volumes of information at very high speed.
To process big data, analytical platforms based on Business Intelligence (BI) have been developed. For example, Microsoft's Power BI is a business analytics service that collects information from various sources and transforms the data into reports based on generated requests.