Python is ruling the programming world and is worth learning in 2022 to master Big Data implications. As per the reports of the TIOBE Index for July 2022, Python ranks in the highest position with a 13.44% rating. All thanks to the global contributors and communities for their constant support.
Likewise, the August 2022 Popularity of Programming Language Index (PYPL) report also showcases the same ranking. Again, Python holds the first position with a 28.11% share. It is the leading programming language in U.S., India, Germany, U.K., and France.
When it comes to preferring a language for data science, newcomers often wonder, “why python for data science”. Well, undeniably, Python is the most appropriate language which subtly meets all business needs of medium to big companies. It is perfect to expedite trouble-free data operations. In this article, we will look at what is Python and Big Data, the need for Big Data programming, why you must learn Python for Big Data, and Big Data Python libraries to help you.
What is Data Analysis?
Data Analysis is simply a process of converting vast raw and unstructured data into more valuable and structured data. In other words, this process incorporates cleaning, processing, and changing the raw data to extract actionable information that supports business strategies by helping them make informed decisions. Data Analysis minimizes the risks for decision-making by giving useful statistics in the form of various bar graphs, tables, images, and more.
What is Python?
Python is a general-purpose and high-level interactive programming language. It is attaining top preference among developers and Big Data enthusiasts due to its simple syntax (similar to English) and the interpreter system.The intriguing part is that Python has fewer syntactical constructions, which makes it highly readable.
It contains a pool of built-in functional capabilities – libraries, data structures, and frameworks. On top of it, anyone (irrespective of coding background) can learn and use Python free of cost. So from beginners to experts, it works for all.
Why is Python Essential?
Python’s large library ecosystem and community promote its popularity and ease of use across many fields in data science.
Python is a highly versatile programming language with a vast ecosystem of tools and libraries designed explicitly for data analysis and manipulation. Popular libraries like NumPy, pandas, and scikit-learn provide robust data manipulation, research, and machine learning capabilities, making Python an ideal choice for big data.
Python offers a range of libraries and frameworks, such as Dask, PySpark, and TensorFlow, which allow for distributed processing and parallel computing. This enables Python to handle large datasets and scale horizontally, making it well-suited for big data analytics, where data processing and analysis must be distributed and parallelized.
Python has a large community of data scientists, engineers, and developers who contribute to developing libraries, tools, and frameworks for big data analytics. This means that Python users have access to a wealth of resources, tutorials, and community support, making it easier to learn and work with Python for big data analytics.
Python offers powerful data visualization libraries such as Matplotlib, Seaborn, and Plotly, which provide comprehensive plotting and visualization capabilities. Effective data visualization is crucial for understanding large datasets, identifying patterns, and gaining insights, making Python a valuable tool for big data analytics.
Python can easily integrate with popular big data technologies like Spark, Hadoop, and Hive, allowing seamless integration into existing big data workflows. This makes Python convenient for big data analytics as it can be easily integrated into existing data processing pipelines and workflows.
Python’s syntax is easy to learn and read, making it a popular choice for rapid prototyping and iterative development. This is particularly valuable in big data analytics, where the ability to prototype and test data analysis algorithms quickly is essential for identifying effective strategies and insights from large datasets.
What is Big Data?
Big Data is fundamentally a high-volume data cluster that consists of different varieties and grows exponentially with time. It is typically large in size and raw form, so traditional programming languages cannot manage it efficiently.Big Data with Python is free from all 4V complications – Volume, Variety, Velocity, and Variability.
In simpler words, Python securely stores, handles, and processes a massive data bundle in no time.
Read to know more: Understand the key characteristics of Big Data
Why is Big Data popular for industrial usage?
Data is pure business, and most firms struggle to tackle much-unstructured data. Due to this, there is a surge in Big Data investments today.
With the rapidly increasing mobile traffic and the advancements in AI and IoT, Big Data is evolving. Statista reports that the Big Data market might rise by 103 billion U.S. dollars by 2027. And Big Data analytics by 274 billion U.S. dollars in 2022.
What are critical Big Data challenges?
The critical challenges Big Data is facing are –
- Managing different varieties of huge data clusters with ongoing changes
- High-speed data processing and computation
- Compatibility with traditional programming languages
- Generating accurate business insights
- Skill gap and lack of Big Data professionals
Python has overcome almost all the Big Data challenges. Still, a significant concern is a shortage of experts corresponding to industry demand.
How Does Python Handle Big Data?
Python handles Big Data with preciseness by embodying the below-mentioned three stages by using its built-in libraries –
Stage 1 – Python mitigates memory usage by optimizing different Big Data types.
Stage 2 – For the above, it splits large data sets into chunks to fit data into memory.
Stage 3 – Thereupon, Python implements the Lazy Evaluation concept. It is a call-by-need evaluation strategy where an expression is not evaluated until it is called.
Depending on the requirement and purpose of business needs, there can be more ways.
Why Big Data Scientists must learn Python?
Python is readily graspable for both coders and non-coders. Executing Big Data is daunting; data scientists require a dynamic programming language for faster data handling. Python is best suited for processing Big Data because it performs excellently in tackling mathematics, statistics, and scientific functions. On the whole, Big Data Python covers a wide range of coherent applications, from implementing libraries to scaling code.
Top 10 Eminent Reasons To Use Python for Big Data
From the above, it is evident why Python is becoming the first choice among all programming languages that can be used for Big Data. Here are the top reasons to use Python for Big Data –
- Python is a good fit for integrating web apps while analyzing Big Data.
- Python facilitates better ease, accessibility, and readability while constituting statistical code in the production database (for statistical analysis).
- Python seamlessly assists in achieving goals within time and with better results, no matter what type of Big Data project. Moreover, it easily migrates to any programming language.
Let us deep-dive into the core reasons for using Python in Big Data programming –
1. Open Source Licensing
The non-profit Python Software Foundation developed Python on the community-based model. It supports Windows, Linux, and macOS environments. This licensing standard allows the developers to alter, modify, or enhance the original code following the project requirements. Undoubtedly, Big Data cases are complicated and time-consuming. Therefore, data scientists need a simple, clean, and understandable open-source programming language to handle large data sets and derive actionable insights.
2. Easy to Learn, Read, and Use
Anyone can learn Python quickly in a short period, including non-coders. Its setup does not require complex configurations. You must install the language and start over. Python is a feature-rich language and follows an indentation-based nesting structure. It includes easy-to-understand syntax, readable codes, and automatic assistance for identifying and associating data types. Notably, Python is a scripting language that makes it much faster. In addition, unlike other Big Data programming languages, Python executes in minimal code.
3. Scalable and Flexible
Big Data with Python comes with effortless scalability and flexibility. Working on large data sets is exhaustive, and the data count exponentially increases with time. Python smoothly resolves this problem by increasing the data processing speed, which makes it highly competent for Big Data. Furthermore, it is flexible enough to adapt new capabilities for scripting websites and applications. Thus, it is a perfect fit for different uses in various industries.
4. Portable and Extensible
Python conveniently performs cross-language operations due to being portable. There is no need to write different codes or modify codes for each new machine. The same program runs on all, like Linux, Unix, Windows, macOS, etc. Additionally, Python is extensible because developers can extend their code in other programming languages like C, C++, Java, .Net framework, etc.
5. Robust Library Packages
Python has a multitude of standard libraries and frameworks that makes it ideal for Big Data programming. On a broader level, for scientific computing and analytical needs. These robust libraries make coding minimal, easier, and faster. Specifically suitable for Big Data applications, it helps meet the following data needs:
- Data Visualization
- Data Analysis
- Statistical Analysis
- Machine Learning
- Numerical Computing
The most famous Big Data Python libraries are – Pandas, Numpy, SciPy, Scikit-learn, Dask, and Dmelt.
6. Ultra Data Processing Support and Speed
Big Data with Python packages can load large data sets because the inbuilt features do not limit data processing. Moreover, they are beneficial in identifying and managing unstructured data, predominantly social media data like audio, images, and text. These handle the data using different file formats such as CSV, XML, HTML, SQL, JSON, etc.
The best part is that such rigorous data processing does not affect the speed. Python accelerates high processing and executes data codes in no time. It enables prototyping ideas and works in a multi-user development environment to expedite coding. However, Python does not compromise the transparency between code and execution during this complete procedure.
7. High compatibility with Hadoop
Python is firmly compatible with Hadoop as both are open-source platforms. Due to this, Python smoothly establishes inherent capability between Hadoop and Big Data using its Pydoop package.
It benefits the developers in the following ways –
- Provides convenient APIs
- Uses natural language for search operations
- Performs complete text-based data processing
- Executes effortless data indexing
- Facilitates uncomplicated data conversion
8. Data Visualization
Data visualization is critical for detecting and understanding the hidden patterns, trends, layers, and relationships within data sets. The most formidable challenge for data scientists is plotting and analyzing high-volume data using traditional programming languages like R. The Python Big Data libraries resolve this problem entirely by making data simplified, clean, easy to absorb, and actionable.
Some popular visualization library packages are Matplotit, Plotly, NetworkX, Pyga, ggplot, Seaborn, Altair, etc. They offer decent data insights and the privilege of building charts, graphical plots, and web-ready interactive plots.
9. Programming and Platform Scope
Python is a multipurpose programming language devised on Object-Oriented Programming (OOPs) Concepts. As a result, Python easily supports cross-platform developments such as web apps, mobile apps, multi-touch apps, data processing apps, innovative GUIs, etc.
Besides, Python for Big Data reinforces high-level data structures to simplify and accelerate the speed of data operations. Like it includes sets, linked lists, tuples, and dictionaries. In addition, Python utilizes concepts such as data frames, matrix operations, and others for scientific computing operations.
10. Large community support
The Big Data Python has super active and progressing community support. They regularly contribute to advancing Python by devising cutting-edge packages that can expand its core functionalities. Furthermore, the community assists the aspirants and existing professionals like administrators, developers, analysts, architects, and data scientists. The subject matter experts address and resolve the queries in real-time. GitHub/GitLab, Codementor, and Stack Overflow are popular communities. However, simple Google Search plus YouTube Videos are also best buddies.
To conclude, Python and Big Data perfectly complement each other. And provide substantial computing capabilities in dealing complexities of Big Data projects.
Libraries That Make Python Useful for Big Data
When it comes to dealing with data hassle-free, everybody prefers to do data analysis with Python. It has over 137000 libraries and is constantly adding up more to keep pace with ongoing advancements. These in-built libraries are well enough to crunch any large data set and streamline multiple tasks in seconds.
Here we have discussed the top Big Data Python libraries which will answer the question “why python for data science” –
Pandas (Python Data Analysis) work on data munging, cleaning manipulation, and analysis. It enables Big Data scientists to build fast and flexible data structures in tabular and multidimensional formats.
They speed up data wrangling and facilitate high-level abstraction. For missing data, Pandas use meaningful syntax and rich feature sets. It also includes top-level data structure and manipulation tools.
Pandas have the privilege of creating self-function for developers and executing it across different data series. They are highly suitable for time series, statistics, finance, neuroscience, and ETL applications.
TensorFlow is an open-source Python library that broadly works in various scientific fields, especially in building machine learning applications and deep neural networks. It assists data scientists in detecting and deciphering patterns, establishing correlations, and implementing analogous reasoning.
TensorFlow identifies the structure based on type, shape, and rank criteria. It includes pipelining system smoothly trains multiple neural networks and GPUs, along with parallel computation for executing complex models.
TensorFlow facilitates high-standard graph visualizations. Furthermore, it lowers error possibilities by 50 to 60%. Notably, Google effectively supports its library management, making TensorFlow scalable, updated, and easier to implement. It is exceptionally advantageous for Video detection, time-series analysis, Speech and image recognition, and text-based applications.
NumPy (Numerical Python) is a general-purpose array processing package backed by the N-dimensional array powerful feature. It is an essential library of Python that makes almost all scientific computations workable. It supports –
- High-level logical and mathematical functions
- Liner algebra and advanced random number generation
- Fourier transform and shape manipulation
- Integration with low-level languages like C, C++, and Fortran.
NumPy addresses the sloppy performance and boosts it by providing multidimensional arrays and matrices. Additionally, it is fast, compact, and comes with vectorization. NumPy vastly functions in data analysis and creates the base of other library packages like SciPy, Scikit-learn, and Matplotlib.
Matplotlib in Big Data with Python offers 2D plotting graphics and appealing data visualizations. A powerful library package for data scientists where they can create bar charts, scatter plots, histograms, error charts, power spectra, etc.
The main feature of Matplotlib is providing an object-oriented API that easily embeds the plots in applications. For this, it uses GUIs like Tkinter & wxPython. The best part is that it consumes low memory and facilitates coherent runtime. Above all, Matplotlib is free, the perfect alternative to MATLAB, and supports a broad range of OS and output types. As a result, it is the go-to for correlation analysis, outlier detection, and forecasting business insights.
SciPy (Scientific Python) extends NumPy and is widely utilized for Big Data projects, primarily in scientific and technical computing. It includes built-in commands and functions for dealing with differential equations, data manipulation, and visualization.
SciPy is incredibly beneficial in the following –
- Advanced-level science and engineering tasks
- Optimization of algorithms
- Signal and multidimensional image processing
- Integration and Interpolation
- Resolving Linear Algebra, Fourier Transform, and Sparse matrices
Apart from these, the other prominent Big Data Python libraries are MlPy, SymPy, Dask, Dmelt, Scikit-learn, Theano, NetworkX, Vaex, Modin, PySpark, and PyTorch.
Python and Big Data are inseparable! Python is the elementary step for attaining excellence and a flourishing career in Big Data Technology. Professionals cannot analyze, process, and extract information from a complicated huge data cluster without programming. Therefore, a language framework plays a key role.
Traditional programming languages like C, C++, Java, R, etc., are comparatively tricky to learn and apply, particularly those with a non-coding background. But in the case of Python, there are no such restrictions. It is a beginner-friendly programming language that uses basic English and requires only logical reasoning to perform maths.
Many free resources are available online, like community forums, YouTube Tutorials, and Guide Blog series to learn the Big Data programming language. Python is not confusing, problematic, or scary. In fact, it helps Big Data professionals streamline diverse data operations.
Frequently Asked Questions (FAQs)
1. Why is Big Data programming an essential skill?
Coding is a prerequisite when it comes to handling Big Data projects. It is the foundation for applying data science to implementing visualization and statistical packages. There are many Big Data programming languages like Java, R, C++, Python, etc. However, Python is the top choice of every developer.
2. Why is Python highly important for data?
Python for Big Data is vital because it has the intense capacity to deal with high-volume data sets. The advantage lies in its seamless operation with structured and unstructured data types. Additionally, Python’s built-in data-oriented libraries make it multifunctional and time efficient.
3. Why is Python best for Big Data projects?
Processing and analyzing Big Data in python is much more convenient than in traditional programming languages. Python supports all OS environments, runs on fewer lines of code, and is easily expandable to other languages. As a result, developers and data scientists find Python a handy, reliable, robust, and high-speed framework.
4. Why do big companies now majorly use Python?
Big companies like Google, Facebook, Instagram, Quora, Reddit, Dropbox, Netflix, and Spotify are using Python for Big Data. Their ground problem lies in working on a wide range of information that needs a streamlined process.
The main objective behind using Python is to boost efficiency. As well as reduce data loading memory and time consumption. It helps automate iterative processes, makes deployments faster, and enhances business operations.
5. Which Python library is widely used for Big Data?
It purely depends on the Big Data project or application’s type, requirement, functionality, and goal. However, Pandas (Python Data Analysis) is the lifeblood of data science and Big Data in Python, widely used for data manipulation, analysis, and cleaning. Along with NumPy for scientific computing and Matplotlib for plotting plus visualization.
6. What is Pydoop in Python? How does it solve Big Data problems?
Pydoop is an interface package that provides exceptional assistance to Hadoop. It allows access to Hadoop Distributed File System (HDFS) API for reading and writing information on global file systems and directories. Pydoop solves complex Big Data problems by providing MapReduce API. This API utilizes high-level data science concepts like Record Reader and Counter. As a result, it takes minimal work to write Hadoop MapReduce programming. Further, making Python a good fit for Big Data.
7. What is Anaconda? How does it amplify Python for Big data?
Anaconda is an open-source software package with built-in machine learning, data science, visualization, deep learning, and large data processing libraries. It is a single installation setup that supports Python and R programming languages. Anaconda’s primary goal is to streamline package and environment management plus deployment.
With over 300 feature-rich libraries, Ananconda has improved the speed performance of Python. As a result, every version of Python becomes faster and more optimal for Big Data analytics.
8. What are IDEs? Which are Python-Specific IDEs?
Integrated Development Environments (IDEs) are simply coding tools to write, test, and debug the code. Spyder, Pycharm, Rodeo, Thonny, and Atom are some Python Big Data IDEs covering all major data-orientated computation and analytics aspects.
9. What are Python Notebooks?
Jupyter Notebook is the standard for Big Data Python. It is also an open-source IDE. However, Jupyter includes browser-based coding, visualizations, equations, and text altogether.
Jupyter Notebook has no dependency on the environment setup/multiple tools. Thus, it is ideal for documenting, visualizing, and analyzing data on a single page. On the whole, it provides a web-based interactive computational development experience. In addition, expanded functionality in Python with over 40 different programming languages like Julia, Scala, R, etc.
10. Why should you learn Big Data, and what are the career prospects?
Big Data is a fast-paced domain in the IT industry and comes with an array of global opportunities. Mid-level to large sizes firms are improving their business operations and decision makings entirely data-driven using Big Data technology. On top of it, most start-ups today are seeing a significant surge in funding and investments, specifically for Big Data.
The top industries hiring Big Data scientists alone in 2022 are BFSI, Media & Entertainment, Retail, Fintech, eCommerce, Telecommunications, Automotive, Mining, Oil & Gas, Digital Marketing, and Cyber Security.
Undoubtedly, Big Data is high in demand. However, there is a shortage of professionals on the work front due to the skill gap.
So, you can lead the frontier in IT by investing in a suitable Python and Big Data course with an industry-relevant curriculum. The prominent Big Data roles across industries are –
- Big Data Engineer
- Big Data Scientist
- Big Data Administrator
- Big Data Architect
- Hadoop Architect
- Big Data Analyst
- Big Data Developer
- Data Visualization Developer
- Business Intelligence Developer