However, Spark SQL with the DataFrames and Spark Machine Learning enable Data Scientists who want to develop in Python of increasing their program’s performances using a cluster. Python is one of the most famous programming language used by Data Scientists who develop programs in order to process Feature Engineering and Machine Learning algorithms by using rich APIs like Scikit-Learn and Pandas on a single multi-cores server. Step 11: Deploying your Python-Spark application in a Production environment Introduction Step 10: Executing your Python-Spark application on a cluster with Hadoop YARN Step 9: Reading a CSV file directly as a Spark DataFrame for processing SQL Step 8: Executing your Python-Spark application with Eclipse Step 7: Creating your Python-Spark project “CountWords” Step 6: Configuring PyDev with Spark’s variables Step 4: Configuring PyDev with a Python interpreter Let’s have a look under the hood of PySpark