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Big Data Analysis

What Tools Facilitate The Big Data Analysis Process In Python?

Specialists In The World Of 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 use properly. In this article, we introduce the most important data science tools available for big data processing. 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 companies in the field of technology use data science and big data processing techniques to extract knowledge from non-structured, structured, and semi-structured organizational data.

Big data processing

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

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

Most data analysis problems that we need to solve 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 are large enough to be used by common computing systems such as personal computers to process them, they can solve without resorting to large data processing methods.

For this reason, big data analysis and processing methods are mostly used in connection with solving analytical problems. In general, big data processing techniques are used to solve problems when we need to process large volumes of data and computing systems do not have the processing power needed to analyze large data.

What tools are available for big data processing?

One of the most important data storage and processing models that have a distributive approach used by data scientists is the MapReduce programming model.

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, a list or set of table colors and the number of tables in each specific color groupings are provided to the programmer or data scientist.

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

In addition, other programming libraries make it possible for 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 use Python-developed data tools, packages, and libraries to implement big data processing applications.

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. The Hadoop Library, based on cluster computing, 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 through which data can send to Hadoop. Of course, the choice of libraries and programming packages for big data processing and data science depends on various factors such as ease of use, the infrastructure needed to implement, and the specific application that the specialist needs.

Spark Library

The Spark library is typically used to process data obtained in the form of stream data (such as real-time data, log data, and data collected by application programming interfaces) because the Spark library is associated with With this model, the data performs better. The Spark Library, like the Hadoop Library, was developed by the Apache Software Foundation.

 Python and big data processing

Almost all new programmers in artificial intelligence and big data ask what Python language allows them to process big data and analyze it. For the past few years, Python, along with other languages ​​such as R, Julia, and Matlab, has been one of the most important tools for data scientists in coding data science projects and extensive capabilities for programmers in big data analysis and processing. Puts.

While some data scientists and programmers in artificial intelligence use programming languages ​​such as Java, R, Julia and 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 for Python have evolved and become so powerful that data scientists prefer to go to Python first to analyze and process large amounts of data.

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

If you look at job postings in the field of web programming, machine learning, and similar applications, you will see that Python is a constant part of these ads.

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

Hadoop open-source tools written by the Apache Software Foundation to provide the software infrastructure needed for distributed computing and big data processing prepared by the Java programming language.

These tools allow programmers to use interconnected computer networks based on 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 of data science by programmers is unbelievable. It convinced the Apache Software Foundation to expand the Hadoop ecosystem for data scientists and machine learning programmers.
  • Nowadays, data scientists and programmers fluent in this language can use Hadoop Streaming, Hadoop plugin, Pydoop library, and Pydopp package for this purpose.

 Use Hadoop in Python instead of Java.

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

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

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 a bit of technical knowledge. Conversely, to install, implement, and run the Python programming environment, you must install Python on the system and perform coding through the command line interface or integrated development environment.
  • In the Java programming environment, the written 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 written to write a program in Java is far greater than the number of lines written 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, formatting, illustrating, analyzing, and accurately analyzing data. Among the powerful libraries that can use in the Python language are the following:

Pandas Library

Undoubtedly, one of the most popular libraries for implementing data transfer and data processing busses in Python is the Pandas Library. The library was developed by a team of data scientists in Python and R languages.

A large community of programmers, data scientists, and analysts oversee 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, tables corresponding to read data, and compiling aggregate analysis based on the type of application being developed.

In addition, the Pandas Library best supports data visualization capabilities. Using the illustrated capabilities embedded in the library, the ability to construct graphs and graphs of the results produced is provided for data scientists.

In addition, there is a set of built-in functions in the Pandas Library that allow you to export data analyzes to an Excel spreadsheet file.

Agate Library

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

Bokeh Library

If you are looking to visualize data in a structured collection, Bokeh is the right 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 illustrations of data with minimal coding.

PySpark Library

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

Hadoop flow tool

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 as an argument to the stdin function.

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

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 and then use the implemented code 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 in the Python environment based on the HDFS file system architecture.

MRJob Library

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

The EMR service 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 the functions of this library without any particular problem.