Once no new row is retrieved, iteration ends. The second step continues until we get some rows after JOIN. In the second step, what ever resultset is generated by seed statement is JOINED with some other or same table to generate another resultset. In a recursive query, there is a seed statement which is the first query and generates a result set. To understand the solution, let us see how recursive query works in Teradata. I have tried something on spark-shell using scala loop to replicate similar recursive functionality in Spark. Also I was wondering if somehow I can come up with more SQL like solution for recursive queries then it will be easy to implement and modify to incorporate more complex scenarios. I searched for various options online ,even explored Spark GraphX API however I could not find suitable solution. The only challenge I see was in converting Teradata recursive queries into spark since Spark does not support Recursive queries. Also transforming SQL into equivalent HIVE/SPARK is not that difficult now. I don't see any challenge in migrating data from Teradata to Hadoop. At a high level, the requirement was to have same data and run similar sql on that data to produce exactly same report on hadoop too. The requirement was to have something similar on Hadoop also for a specific business application. Recently I was working on a project in which client data warehouse was in Teradata. Step 5: Merge multiple dataset into one and run final query.Step 4: Run the while loop to replicate iteration step.Step 3: Register the dataframe as temp table to be used in next step for iteration.Step 2: Create a dataframe which will hold output of seed statement.Step 1: Declare 2 variables.First one to hold value of number of rows in new dataset & second one to be used as counter.

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