Gain insights into enhancing Python data processing with PySpark. Delve into reading, transforming, aggregating data, and creating user-defined functions, boosting efficiency with Apache Spark.

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Pandas is a popular Python library used to manipulate data, but it has certain limitations in its ability to process large datasets. The Apache Spark analytics library offers significant performance improvements.

This course will help improve your Python-based data processing by leveraging Apache Spark’s multithreading capabilities through the PySpark library. You’ll start by reading data into a PySpark DataFrame before performing basic input/output functions, such as renaming attributes, selecting, and writing data. You’ll move onto transformation functions like aggregation, statistical analysis, and joins before creating custom, user-defined functions. At each step, you’ll get a quick Pandas review before being walked through leveraging the more robust PySpark library to unlock Apache Spark.

By the end of this course, you’ll be able to quickly and reliably process large amounts of data, even stored across multiple files, using PySpark.

From Pandas to PySpark DataFrame

## Saving the updated dataset checkpoint
In this lesson, we’ll learn to save the current snapshot of the data for future use. This makes the newly added column available as a struct object when we load the latest snapshot. This also allows us to access any required field of the struct using the `.` (dot) operator.


# Saving the updated dataset checkpoint
In this lesson, we’ll learn to save the current snapshot of the data for future use. This makes the newly added column available as a struct object when we load the latest snapshot. This also allows us to access any required field of the struct using the `.` (dot) operator.


Introduction

Data Input/Output

Data Transformation

User Defined Function (UDF)

Wrapping Up

Appendix

Apriori Algorithm for Finding Frequent Itemsets with PySpark

UDF: Save a snapshot

Saving the updated dataset checkpoint