Joining 3 or more tables in Spark Dataframe API using Scala | Scenario-based questions | Part -1

Hi Friends,

In today's video, I have explained the procedure for joining 3 tables for solving a business scenario.
In the next videos we will see how to resolve the same using Spark SQL queries.
Please subscribe to my channel and prodive your feedback in the comments section.

[Khader_views]

Really its good capture on real time spark api.

[balupolisetti]

Really good explanation. I tried, Without rank function also we can do the same 🙂

[vaibhavverma]

Hi, can you pls provide the datasets btw you explained very well.

[debasishkhuntia]

Mam hope you are doing good, one request, can you please provide the dummy data that you are processing

[nareshkumar]

Thank you so much posting this, can please share the datasets for this example.

[localmartian]

In the order by on window, you have done ascending order followed by ranking.

Then you do descending after applying rank().
We could also do

And use dense_rank() followed by filter(col('rank') <=5) so that it works even for same revenues
What do u think

[nithinrpillai]

is there a way to join scala dataframes with case sensitive key