Summary

In this chapter, we covered the fundamentals of using Apache Spark for large-scale data processing. You learned how to set up a local Spark environment and use the PySpark API to load, transform, analyze, and query data in Spark DataFrames.

We discussed key concepts such as lazy evaluation, narrow versus wide transformations, and physical data partitioning that allow Spark to execute computations efficiently across a cluster. You gained hands-on experience applying these ideas by filtering, aggregating, joining, and analyzing sample datasets with PySpark.

You also learned how to use Spark SQL to query data, which allows those familiar with SQL to analyze DataFrames. We looked at Spark’s query optimization and execution components to understand how Spark translates high-level DataFrame and SQL operations into efficient distributed data processing plans.

While we only scratched the surface of tuning and optimizing Spark workloads, you learned about some best practices...