Hi Maatari!
How are you doing today?
When you group data by a column in a Delta table, Spark typically has to shuffle the data to get all the same values together. But if your Delta table is already partitioned by that same column, the shuffling is much less because the data is already nicely organized.
For example, if your Delta table is partitioned by store_id, and you want to group by store_id to see total sales per store, Spark can do that faster since it doesn't need to move data around as much.
Also, when you load data from a Delta table into a DataFrame, Spark usually respects the table’s partitioning. So if your table is partitioned by store_id, your DataFrame might also be partitioned that way, which again helps reduce shuffling during operations like groupby.
In short, if you partition your Delta table by the column you plan to group by, it can make your queries run a lot smoother!
Have a good day.
