How to Merge Join Multiple DataFrames in Spark Scala Efficient Full Outer Join
<p>How do I merge / join multiple Spark DataFrames (Scala) efficiently? I want to join a column that is common to all tables, 'Date' below, and get (sort of) a sparse array as a result.</p>

<pre><code>Data Set A:
Date    Col A1   Col A2
-----------------------
1/1/16  A11      A21
1/2/16  A12      A22
1/3/16  A13      A23
1/4/16  A14      A24
1/5/16  A15      A25

Data Set B:
Date    Col B1   Col B2
-----------------------
1/1/16  B11      B21
1/3/16  B13      B23
1/5/16  B15      B25

Data Set C:
Date    Col C1   Col C2
-----------------------
1/2/16  C12      C22
1/3/16  C13      C23
1/4/16  C14      C24
1/5/16  C15      C25

Expected Result Set:
Date    Col A1   Col A2  Col B1  Col B2  Col C1  Col C2
---------------------------------------------------------
1/1/16  A11      A21     B11     B12
1/2/16  A12      A22                     C12     C22
1/3/16  A13      A23     B13     B23     C13     C23
1/4/16  A14      A24                     C14     C24
1/5/16  A15      A25     B15     B25     C15     C25
</code></pre>

<p>This feels like a full outer join on multiple tables, but I am not sure.
Is there some simpler / more efficient way to get to this sparse array without the Join method on DataFrames?</p>