What is the process of grouping in Spark Scala to create a nested DataFrame?

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Grouping in Spark Scala is the process of partitioning data based on a common attribute or key. To create a nested DataFrame, we can follow these steps:

1. Load data into a DataFrame.
2. Identify the grouping key for nesting the data.
3. Group the data based on the grouping key.
4. Aggregate the data as required.
5. Convert the grouped data to a nested DataFrame.

To nest data, we can use the struct function to create a new column of nested data types. We can then use the groupBy function to group the data by one or more columns and apply aggregation functions such as sum, avg, or count to the grouped data. Finally, we can use the agg function to apply the aggregation functions to the grouped data and create a new DataFrame with nested data.

Example code:

import org.apache.spark.sql.functions._

// Load data into a DataFrame
val df = spark.read.format("csv")
  .option("header", "true")
  .load("path/to/data")

// Group data by two columns and nest the data
val nestedDF = df.groupBy("column1", "column2")
  .agg(struct($"column3", $"column4", $"column5").alias("nested_data"))

// Display the nested DataFrame
nestedDF.show()