Apache Spark 3 for Data Engineering and Analytics with Python - Challenge Part 2 - Write Partitioned DataFrame to Parque

This final lecture covers writing a dataframe into a partitioned Parquet file. It begins with creating and arranging the dataframe columns, followed by writing the data into a Parquet file partitioned by year and month. The lecture explains the benefits of partitioning, such as improved performance when working with large datasets. The session concludes with a demonstration of how partitioning organizes data into separate folders for each year and month, enhancing data management and retrieval efficiency.

What steps should be taken to confirm the order of the columns in the dataframe?

What does the term 'partitioning' mean in the context of data management?

How does partitioning improve performance when querying data?