Hello @Jack_sparrow  That should be possible.  You don't need to manually specify partitions or HDFS paths; Spark handles this automatically when you use a DataFrameReader. First, you will need to read the source table using "spark.read.table()". Since table is a Hive partitioned table, Spark will automatically discover and read all 100 partitions in parallel, as long as you have enough executors and cores available. Then, Spark creates a logical plan to read the data.  Repartition the data is next, To ensure you have exactly 10 output partitions and to control the parallelism for the write operation, you can use the "repartition(10)" method. This will shuffle the data to create 10 new partitions, which will be processed by 10 different tasks. And then, write the table. Use "write.saveAsTable()". You must specify the format using ".format("parquet")." ... View more

Hello @Jack_sparrow,  Spark should automatically do it, you can control that with these settings:  Input splits are controlled by spark.sql.files.maxPartitionBytes (default 128MB). If smaller, more splits or parallel tasks will be executed. spark.sql.files.openCostInBytes (default 4MB) influences how Spark coalesces small files. Shuffle parallelism spark.sql.shuffle.partitions (default 200). Configiure around  2–3 times per total executor cores. Also, make sure df.write.parquet() doesn’t set everything into few files only. For that, you can use .repartition(n) to increase the parallelism before writing. ... View more

Thank you for the response. ... View more