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Specialists in Artificial Intelligence are familiar with terms such as data science, Data Analytics, Data Transfer, Machine Learning, Big Data, And The Like.

Big Data Analysis: These terms are classified in a conceptual subset called computer science, and they all refer to future technological advances. All of these new technologies require a data-driven nature.

The data that drives these technologies must first be processed so that they can be used properly. In this article, we introduce the most important data science tools available for big data processing. These are assets that are not expected to be retired or replaced in the short term.

Data science is one of the broad fields of computer science and includes various sub-disciplines such as Data Collection, Data Cleaning, Standardization, Data Analysis, and Reporting.

All technology companies use data science and big data processing techniques to extract knowledge from non-structured, structured, and semi-structured organizational data.

Big Data Analysis

Big data analysis and processing have become one of the most significant research trends in the world. That’s why a good job market has emerged for developers and programmers familiar with these techniques.

For example, a data scientist or programmer proficient in big data processing and analysis can use the tools available in this field to process big data for language analysis (natural language processing), video suggestions (Recommendation Videos), or offering new products, paying attention to marketing data or data obtained from customers.

Most data analysis problems we need to solve in programming projects can be solved by conventional solutions in interaction with machine learning methods.

More precisely, when the data used to model or teach a learning model is large enough to be processed by common computing systems such as personal computers, it can be solved without resorting to large data processing methods.

For this reason, big data analysis and processing methods are mostly used to solve analytical problems. In general, big data processing techniques are used to solve problems when large volumes of data need to be processed, and computing systems do not have the processing power needed to analyze large amounts of data.

What tools are available for big data processing?

The MapReduce programming model is one of the most critical data storage and processing models with a distributive approach used by data scientists.

The model is an optimal solution for big data management and processing that allows data scientists and developers of intelligent algorithms to categorize data using a tag, or more accurately, an attribute, and then use a conversion or aggregation mechanism to refine and reduce mapped data.

For example, if the collected data is related to a set of objects such as a table, the tables are first mapped using an attribute such as color. Then, the data is grouped through the aggregation technique to remove unnecessary information and reduce the volume of data.

At the end of the MapReduce process, the programmer or data scientist is provided with a list or set of table colors and the number of tables in each specific color grouping.

Almost all programming libraries developed for big data processing and data science support the MapReduce programming approach.

In addition, other programming libraries allow programmers and data scientists to manage, process, and perform data mapping operations with a distributed approach. Of course, the above method uses clusters or sets of connected computers in a specific way.

People who plan to use MapReduce models to manage and process big data can implement big data processing applications using Python-developed data tools, packages, and libraries.

Hadoop Library

One of the most widely used and popular libraries for big-data look-down operations is the Hadoop Library, which the Apache Software Foundation supports. Based on cluster computing, the Hadoop Library allows data science professionals to perform tasks related to large data management and processing more quickly and efficiently.

Many libraries and packages have been developed in the Python language to send data to Hadoop. Of course, the choice of libraries and programming packages for big data processing and data science depends on factors such as ease of use, the infrastructure needed to implement, and the specific application the specialist needs.

Spark Library

The Spark library typically processes data obtained as stream data (such as real-time data, log data, and data collected by application programming interfaces). The data performs better because the Spark library is associated with this model. Like the Hadoop Library, the Spark Library was developed by the Apache Software Foundation.

Python and big data processing

Almost all new artificial intelligence and big data programmers ask what Python allows them to process and analyze big data. For the past few years, Python and other languages ​​such as R, Julia, and Matlab have been essential tools for data scientists in coding data science projects and have extensive capabilities for programmers in big data analysis and processing.

While some data scientists and programmers in artificial intelligence use programming languages ​​such as Java, R, and Julia, as well as tools such as SPSS and SAS to analyze data and extract insights from the heart of data, Python uses a variety of programming libraries. The application that they support has a special place.

The libraries and frameworks written for data processing in Python have evolved and become so powerful that data scientists prefer to use Python first to analyze and process large amounts of data.

Python has been recognized as the most popular programming language for several years.

If you look at job postings for web programming, machine learning, and similar applications, you will see that Python is frequently mentioned in these ads.

Most data engineers choose Python as the primary language for big data analysis and processing. As mentioned, the Hadoop library is one of the most important big data processing tools used by data scientists.

Hadoop open-source tools written by the Apache Software Foundation provide the software infrastructure for distributed computing and big data processing, written in Java.

These tools allow programmers to use interconnected computer networks based on a distributed architecture to solve problems requiring large amounts of data and computing.

Python has attracted the attention of programmers in the field of data science programming and big data processing for the following two reasons:

• It has powerful libraries, tools, and software packages for processing, storing, displaying, formatting, and visualizing data.
• Python’s acceptance by programmers in data science is unbelievable. It convinced the Apache Software Foundation to expand the Hadoop ecosystem for data scientists and machine learning programmers.
• Data scientists and programmers fluent in this language can use Hadoop Streaming, the Hadoop plugin, the Pydoop library, and the Pydopp package.

Use Hadoop in Python instead of Java.

One of the most important questions when Hadoop is used in Python for big data processing and analysis is why Python should be used instead of Java when Hadoop and Spark are written in Java.

And almost all large programs, especially at the enterprise level, are written in Java. In response, the two languages ​​differ in their structural features, and in general, Python provides a simpler syntax for programmers to use for data processing.

The most important differences between the two languages ​​are the following:

•  Installing, implementing, and executing a Java development environment is more complex than Python; configuring settings related to environment variables, configuring XML files related to software dependencies, and the like requires some technical knowledge. Conversely, to install, implement, and run the Python programming environment, you must install Python on the system and code using the command-line interface or an integrated development environment.
• In the Java programming environment, the code must first be compiled and then executed. At the same time, in Python, the code must be written by the programmer and then executed line by line in the command-line interface.
• Typically, the number of lines required to write a program in Java is far greater than the number needed to write the same program in Python.

Data management and processing libraries in Python

As mentioned, powerful libraries, tools, and programming packages are written in Python to store, display, format, illustrate, and analyze data accurately. Among the powerful libraries that can be used in the Python language are the following:

Pandas Library

Undoubtedly, the Pandas Library is one of the most popular libraries for implementing data transfer and data processing buses in Python. A team of data scientists in Python and R developed the library.

A large community of programmers, data scientists, and analysts oversees the Pandas development process, troubleshooting and adding new features to the library.

The Pandas library provides developers with fully functional features for reading data from various sources, creating data frames and tables from the read data, and performing aggregate analysis based on the type of application being developed.

In addition, the Pandas library provides strong data visualization capabilities. Using the library’s illustrated capabilities, data scientists can construct graphs of the results.

In addition, the Pandas Library provides a set of built-in functions that allow you to export data analysis results to an Excel spreadsheet.

Agate Library

The Agate library is relatively new compared to the available examples. The above library can analyze and compare spreadsheet files and perform statistical calculations on data. Edit has a simpler syntax and fewer dependencies than Pandas. In addition, it offers interesting features for data visualization and chart creation.

Bokeh Library

If you want to visualize data in a structured collection, Bokeh is the correct library. The library can use data analysis libraries such as Pandas, Agate, and similar examples. The library allows programmers to generate efficient graphical diagrams and data illustrations with minimal coding.

PySpark Library

PySpark, Spark’s application programming interface for Python, helps data scientists quickly implement the infrastructure needed to analyze and reduce data sets. In addition, because the Spark library includes a set of machine learning algorithms, it is used to process and manage large amounts of data and solve machine learning problems.

Hadoop flow tool

The Hadoop Streaming tool is one of the most widely used methods of using Hadoop in Python. Flow generation is one of the hallmarks of the Hadoop library. This feature allows programmers to map code written in Python or other programming languages ​​to perform operations and send it to the stdin function as an argument.

To be more precise, developers can use Java as a wrapper and redirect Python code to the Hadoop library’s stdin function to perform mapping operations in Java.

Hadoop plugin

The plugin is used to stream Hadoop and uses the Cyton library for mapping-reduction operations in Python. Good application documentation has been prepared for this plugin so that developers can use it easily.

Pydoop package

The package allows developers to code programs related to big data processing in Python. The implemented code can then be used to directly communicate with data stored in the Hadoop cluster and perform mapping-reduction operations.

This is done through the HDFS API application programming interface contained in the Pydoop package. The interface allows programmers to read and write data based on the HDFS file system architecture in the Python environment.

MRJob Library

MRJob is another library available to data scientists for Python mapping reduction and big data processing. Yelp designed the most widely used programming package for big data processing in Python. The library supports Hadoop, Google Cloud Dataproc service, and Amazon Elastic MapReduce service.

The EMR is a web service designed by Amazon to analyze and process large amounts of data through Hadoop and Spark. Good documentation and tutorials have been prepared for this library so that programmers can use its functions without any particular problem.

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