Union, UnionByName, and DropDuplicates
Explore how to combine and manage Spark DataFrames using union to merge datasets with identical schemas, unionByName to align columns by name despite schema order, and dropDuplicates to remove specific duplicate rows based on selected columns. Understand practical use cases with HR data examples and learn how these transformations optimize big data workflows.
We'll cover the following...
Union
The union transformation allows us to combine two DataFrames, thus producing a new one containing the rows from both.
This operation has the following characteristics:
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The schemas of both DataFrames have to be identical. This doesn’t detour much from the classical SQL UNION operation available in RDBMS.
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Duplicate records are preserved and aggregated to the final results.
We are going to first present a graphical representation of this transformation, which illustrates an interesting ...
The union transformation merges and piles up one DataFrame after the another. No exchange of ...