Using PySpark for parallel data processing
As discussed previously, Apache Spark is written in Scala language, which means there is no native support for Python. There is a large community of data scientists and analytics experts who prefer to use Python for data processing because of the rich set of libraries available with Python. Hence, it is not convenient to switch to using another programming language only for distributed data processing. Thus, integrating Python with Apache Spark is not only beneficial for the data science community but also opens the doors for many others who would like to adopt Apache Spark without learning or switching to a new programming language.
The Apache Spark community has built a Python library, PySpark, to facilitate working with Apache Spark using Python. To make the Python code work with Apache Spark, which is built on Scala (and Java), a Java library, Py4J, has been developed. This Py4J library is bundled with PySpark and allows the Python...
