Are you aware of aggregations? Have you used aggregations in Spark?

Yes, I am familiar with aggregations in Spark. In Spark SQL, you can perform aggregations using the DataFrame API with methods like groupBy and agg. For example:

val df = spark.read.json("data.json")

val result = df.groupBy("category").agg(sum("sales"), avg("price"))

This groups rows by the 'category' column and calculates the sum of sales and average price for each group. Spark also provides the reduceByKey transformation on RDDs for key-value pairs, which is efficient for large-scale aggregation. Additionally, Spark SQL supports window functions for more advanced analytics. Aggregations in Spark are optimized through Catalyst and Tungsten, enabling efficient execution on distributed data.