Dynamically Pivot/Transpose Rows to Columns in Hive/Spark - sql

Question

I have Quaterly basis Data and Data keeps Growing dynamically as Quater Grows-
qtr dimvalue percentage
FY2019-Q1 XYZ 15
FY2019-Q1 ABC 80
FY2019-Q1 PPP 5
FY2019-Q2 XYZ 10
FY2019-Q2 ABC 70
FY2019-Q2 PPP 20
When the Number of Quarters are less i am manually editing the query every time and trying the query as below to transpose it-
SELECT dim_value,SUM(Quater_1) as Quater_1,SUM(Quater_2) as Quater_2 from
(
SELECT dim_value,
CASE WHEN qtr='FY2019-Q1' THEN percentage END AS Quater_1,
CASE WHEN qtr='FY2019-Q2' THEN percentage END AS Quater_2 FROM
( select * from schema.table where qtr in ('FY2019-Q1','FY2019-Q2'))t2 order by dim_value
)t1 group by dim_value;
dimvalue Quater_1 Quater_2
XYZ 15 10
ABC 80 70
PPP 5 20
But my Query is how can i active this in a dynamic way and more robust way to transpose rows into columns and keeping in mind the growing quaters and also have proper Quaterwise column names as the Quater grows.
Altogether i am looking for how can perform this using a more dynamic Query be it using Hive or Spark-SQL or if any suggestions to perform it?
Thanks for the Help

Answer 1

You could easily do such pivot using Dataset API if that's doable for you.
spark.table("schema.table").groupBy("dimvalue").pivot("qtr").sum("percentage").show
+--------+---------+---------+
|dimvalue|FY2019-Q1|FY2019-Q2|
+--------+---------+---------+
| PPP| 5| 20|
| XYZ| 15| 10|
| ABC| 80| 70|
+--------+---------+---------+
With SQL the only way is to build it dynamically.