The current raw data is on Hive. I want to do a join of several partitioned terabytes Hive tables, and then output the result as a partitioned Hive table in Parquet format.
I am considering to load all partitions of Hive tables as Spark dataframes. And then do join, group by, and etc. Is this the right way to do?
Finally I will need to save the data, can we save Spark dataframe as a dynamic partitioned Hive table in Parquet format? How to deal with the metadata?
If one of the several data set is sufficiently smaller than the other, you may want to consider using Broadcast for data transfer efficiency.
Depending on the nature of the data, you could try group by, then join. So each machine only need to process a specific set of data, reduce the amount of data transferred during task run.
Hive supports storing data into Parquet format directly. https://cwiki.apache.org/confluence/display/Hive/Parquet. Have you given a try?
