Apache Spark is an open-source integrated computing environment that is used when handling large data sets. Scala and Python are some of the programming languages that it offers APIs. There is PySpark which is the Python API for Spark as there is Spark which is used with Scala most of the time. In this blog, we will discuss what are PySpark and Spark and what is the difference between them.
Table of Content
Spark
Spark is an open-source, in-memory data processing system for large-scale cluster computing with APIs available in Scala, Java, R, and Python. The system is known to be fast, as well as capable of processing large volumes of information concurrently in a distributed network.
PySpark
Apache Spark is an open-source cluster computing framework, and PySpark is its Python API. It helps the developers who use Python to utilize the capability of Spark, providing big data plumbing and processing in the Python language.
Spark vs PySpark
|
Aspect |
Spark |
PySpark |
| What it is | It is the main framework, mostly used with Scala. | A subproduct of Apache Hadoop that is integrated with Python. |
| Programming Language | Uses Scala (a programming language). | It uses Python which is a common language in handling big data. |
| Difficulty to use | Scala, as compared with other programming languages, is also even more challenging to learn for newcomers. | Python language is more preferable compared to Java and this language is easy for beginners. |
| Performance | Quicker because Spark transcended it, note that Spark is communicated in this language. | A little slower because of interactions between Python language and Spark system. |
| Library Support | Most suitable for users who wish to have complete control of the options being used. | Compatible with Python Libraries such as Pandas and NumPy. |
| When to use | Economical for large-scale computations when the speed of computation is the most important factor. | Especially useful for big data scientists who wish to engage in Python programming. |
| Community | Pervasive among Spark developers and those who are very familiar with using the tool. | Used mostly by Python developers, or even data scientists. |
| Features | Has complete visibility of all the features offered by Spark. | Features of Spark may not be as easily accessed depending on the particular features being used. |
Conclusion
So far in this blog, we have learned what are Spark and PySpark and what are the differences between them. Spark is an open-source, in-memory data processing system for large-scale cluster computing with APIs available in Scala, Java, R, and Python while PySpark is its Python API. It helps the developers who use Python to utilize the capability of Spark.