Methods and algorithms for processing big data using quantum algorithms

Big data processing methods
There are many big data techniques designed to efficiently analyze and interpret large data sets. Big data processing systems are frameworks, that is, frames, for the use of which it is necessary to interface them with other frameworks, user application software and a data storage system. The analytical report Big Data Analytics Market Study, 2017 Edition [12] provides the following diagram of Big Data infrastructures implemented in enterprises, presented by enterprise size (Table 1).


Table 1. Big data infrastructures by enterprise by number of employees
Distributed Data Storage and Processing: To process large volumes of data, distributed data storage and processing is often used. This involves dividing data across multiple nodes or servers to enable parallel processing of data.
Machine Learning Algorithms: Machine learning techniques such as clustering, classification and regression are often used to analyze big data. These algorithms allow you to identify hidden patterns, dependencies and trends in the data.
NoSQL Databases: NoSQL databases provide a flexible and scalable model for storing and processing large volumes of unstructured and semi-structured data. These databases provide high performance and horizontal scaling.
Stream Data Processing: Stream data processing techniques are used to process data in real time or in streaming mode. These methods allow you to process incoming data in real time and respond to it immediately.
Data Visualization: Data visualization is used to present large amounts of data in a visual and understandable format. Visual tools help you identify trends, patterns, and relationships in your data.
Cloud Computing: Cloud computing provides powerful computing resources and data storage to process large volumes of data. This allows you to efficiently scale computing and data storage depending on your needs.
Hadoop big data processing systems
Hadoop is a popular and powerful big data processing and storage system that allows efficient data processing on computer clusters [9]. It consists of several key components:
Hadoop Distributed File System (HDFS): HDFS is a distributed file system that is designed to store large amounts of data across multiple nodes in a cluster. HDFS provides fault tolerance and ultra-high scalability by breaking data into blocks and distributing them across nodes. (Figure 4)
Hadoop MapReduce: MapReduce is a data processing model used in Hadoop to process large amounts of data in parallel. It divides the tasks into two phases: the Map phase, where the data is processed independently on different nodes, and the Reduce phase, where the Map results are combined to produce the final result.

Fig.4 Schematic principle of MapReduce operation
YARN (Yet Another Resource Negotiator): YARN is a resource management framework that manages computing resources in a cluster and assigns jobs to execution. It is responsible for accounting and using resources, as well as scheduling and executing tasks on cluster nodes.
Hadoop Common: Hadoop Common is a set of utilities and libraries that provide basic functionality and tools for working with the entire Hadoop system. It provides the necessary functionality to work with HDFS, MapReduce and other components.
Hadoop Ecosystem: A wide ecosystem of tools and frameworks has formed around Hadoop that expand its functionality and provide additional capabilities for data processing and analysis. Examples of such tools include Apache Pig, Apache Hive, Apache Spark, and many others.
Hadoop provides a distributed and scalable platform for processing big data. It is widely used in various industries that require processing, storing and analyzing large amounts of data, for example, in the banking sector, telecommunications, medicine and many others.