-A Spark DataFrame is created from the dataset.

-A temporary table of the original DataFrame is created.

-A query is written that returns the average price, rounded to two decimal places, for a four-bedroom house that was sold in each year.

-A query is written that returns the average price, rounded to two decimal places, of a home that has three bedrooms and three bathrooms for each year the home was built.

-A query is written that returns the average price of a home with three bedrooms, three bathrooms, two floors, and is greater than or equal to 2,000 square feet for each year the home was built rounded to two decimal places.

-A query is written that returns the average price of a home per "view" rating having an average home price greater than or equal to $350,000, rounded to two decimal places. (The output shows the run time for this query.)

-A cache of the temporary "home_sales" table is created and validated.

-The query from step 6 is run on the cached temporary table, and the run time is computed.

-A partition of the home sales dataset by the "date_built" field is created, and the formatted parquet data is read.

-A temporary table of the parquet data is created.

-The query from step 6 is run on the parquet temporary table, and the run time is computed.

-The "home_sales" temporary table is uncached and verified.