In this chapter, we have introduced you to the Hadoop ecosystem, including the architecture, HDFS, and PySpark. After this introduction, we started setting up your local Spark instance, and after sharing variables across cluster nodes, we went through data processing in Spark using both RDDs and DataFrames.
Later on in this chapter, we learned about machine learning with Spark, which included reading a dataset, training a learner, the power of the machine learning pipeline, cross-validation, and even testing what we learned with an example dataset.
This concludes our journey around the essentials in data science with Python, and the next chapter is just an appendix to refresh and strengthen your Python foundations. In conclusion, through all the chapters of this book, we have completed our tour of a data science project, touching on all the key steps of a project and presenting...
