Apache Spark is one the most widely used framework when it comes to handling and working with Big Data and Python is one of the most widely used programming languages for Data Analysis, Machine Learning and much more. So, why not use them together? This is where Spark with Python also known as PySpark comes into the picture.
Spark is used in the industry a lot because of its rich library set, Python is used by the majority of Data Scientists and Analytics experts today. Integrating Python with Spark was a major gift to the community. Spark was developed in Scala language, which is very much similar to Java. It compiles the program code into bytecode for the JVM for spark big data processing. To support Spark with python, the Apache Spark community released PySpark.
Although Spark was designed in scala, which makes it almost 10 times faster than Python, but Scala is faster only when the number of cores being used is less. As most of the analysis and process nowadays require a large number of cores, the performance advantage of Scala is not that much. For programmers Python is comparatively easier to learn because of its syntax and standard libraries. Moreover, it’s a dynamically typed language, which means RDDs can hold objects of multiple types.
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