Working with PySpark
Currently Apache Spark with its bindings PySpark and SparkR is the processing tool of choice in the Hadoop Environment. Initially only Scala and Java bindings were available for Spark, since it is implemented in Scala itself and runs on the JVM. But on the other hand, people who are deeply into data analytics often feel more comfortable with simpler languages like Python or dedicated statistical languages like R. Fortunately Spark comes out-of-the-box with high quality interfaces to both Python and R, so you can use Spark to process really huge datasets inside the programming language of your choice (that is, if Python or R is the programming language of your choice, of course).
The integration of Python with Spark allows me to mix Spark code to process huge amounts of data with other powerful Python frameworks like Numpy, Pandas and of course Matplotlib. I also know of software development teams which already know Python very well and try to avoid to learn Scala in order to focus on data processing and not on learning a new language. These teams also build complex data processing chains with PySpark.
When you are working with Python, you have two different options for development: Either use Pythons REPL (Read-Execute-Print-Loop) interface for interactive development. You could do that on the command line, but Jupyter Notebooks offer a much better experience. The other option is a more traditional (for software development) workflow, which uses an IDE and creates a complete program, which is then run. This is actually what I want to write about in this article. Read More