PySpark vs Python: What Are The Main Differences?
The software industry is a comprehensive yet complex hub of technical terms and techniques. Today, the sector is so vast that keeping a track of any particular tool and its many branches is no easy feat for even experts of the field. The same is the case with Python – an all-popular programming language. Aspiring developers today often face the dilemma of choosing a side in the matters of PySpark vs Python.
While both the tools are interlinked in many ways, they also feature a host of differences. Here is the first thing you must understand about these two tools – PySpark is a Python-based API for utilizing the Spark framework in combination with Python.
Spark is a Big Data computational engine, whereas Python is a programming language. To better understand which one serves as the right solution, let’s dive deep into the features and advantages of PySpark vs Python, as well as the key differences between the two.
What Is Python?
Created by Guido van Rossum and first released in 1991, Python is designed to promote code readability. Python is simple, straightforward, and versatile. It is an ideal choice for a wide range of projects, from simple web applications to operating systems. As its easy-to-learn syntax promotes readability, the cost of program maintenance is significantly reduced.
The best part about Python is that it is both object-oriented and functional, allowing programmers to think of code as both data and functionality. Any programmer with significant experience in coding can pick up this programming language.
As such, it happens to be a preferred one among today’s software developers. Moreover, Python is compatible with all operating systems, meaning that it can help you build native applications for both Windows and Mac computers.
Top 3 Advantages Of Python
Simple To Learn, Simple To Use:
No matter the development environment, Python is one of the simplest languages to work with, even for beginners. The programming language makes it easier for developers to build intuitive applications or websites with less coding. This also makes for faster prototyping and leaves more room for testing different code concepts.
It is not just simple to use, but also simple to understand and read, especially for the non-technical departments. Although the language is object-oriented, it also allows developers to integrate top-notch functionality.
Great Community Support:
Python is an open-source tool, meaning that it is free to be leveraged by any and everyone who aspires to master it. With a basic knowledge of programming languages, developers can start working with Python in a matter of minutes.
Given that Python has been popular for decades, it now has impressive, and active community support. Python experts from across the globe contribute to the language’s development and support forums to provide learning developers with quick solutions to tedious problems.
Enhanced Productivity & Efficiency:
One of the key purposes for the creation of Python is to bring in a certain degree of efficiency to development processes. Being a flexible programming language, Python ensures enhanced productivity.
Owing to its dynamic typing and concise syntax, it is considered to be far more productive than its alternatives like Java. The integration and control features of the Python programming language allow the codes to run more efficiently.
What Is PySpark In Python?
PySpark is an application program interface for Apache Spark in Python. What is PySpark used for? The main objective of this API is to enable developers to write Spark applications using Python APIs. Secondly, it also provides the PySpark shell to help them best analyze massive datasets in complex development environments.
In simple terms, it is an API that allows you to make the best of both worlds – Python & Spark. It is the best medium to leverage the simplicity of Python and the brilliance of Spark to analyze data.
Being a subtle representation of Spark, PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, Machine Learning, and Spark Core. The API is also well-known for quick integration in the Python programming language.
If a project involves working with heavy datasets, PySpark is the go-to framework. It is a blessing for data engineers who are responsible for carrying out computations on huge datasets.
Top 3 Advantages Of PySpark
Quick Data Processing:
PySpark, being a bridge between Python and Spark, is a powerhouse of benefits. With this tool, developers can process data stored on the disk at a quicker pace. In addition, the data processing performance happens 100 times faster in the memory.
PySpark achieves this by brilliantly decreasing the number of read-write to disk. Given that Spark has 80 high-level operators, it is easy for developers to build parallel applications.
Real-Time Stream Processing:
Of all the languages, PySpark is your best bet when it comes to real-time stream processing. When developers work with Hadoop MapReduce, they have the means to manage the available data, but not in real-time. This is where PySpark Streaming enters the picture as the ideal solution.
Fault Tolerance In Spark:
PySpark does a great job of identifying and fixing the malfunction of any node in the cluster. This helps prevent any loss of data. How so? The tool enables the use of Spark abstraction-RDD for fault tolerance.
Key Differences Between PySpark vs Python
| PySpark | Python |
| PySpark is a Python-based API for best utilizing the Spark framework in combination with Python. | Python is a programming language with simplified syntax, created by Guido Van Rossum. |
| PySpark is exclusively used to analyze and make the most of Big Data. | Python is used to unravel the many wonders of Artificial Intelligence, Machine Learning, Big Data, and more. |
| PySpark uses an API in Python called the library Py4j. | Python features a standard library that supports a broad range of features such as databases, automation, text processing, and scientific computing. |
| To master PySpark, basic knowledge of Spark and Python is a must. | To master Python, a developer must have basic knowledge of any programming or must be familiar with the fundamentals of coding. |
| The API is developed and licensed under Apache Spark Foundation. | The programming language is licensed under Python. |
When it comes to PySpark vs. Python performance, the debate is a long one. In the end, it boils down to what your project requirements are. Once you have clarity on your project goals, it becomes easier to decide on which one of the tools best fits the bill.
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