PySpark — Retrieve Unmatched rows from two Dataframes
Data integrity refers to the quality, consistency, and reliability of data throughout its life cycle. Data engineering pipelines are methods and structures that collect, transform, store, and analyse data from many sources.
If you are working as a PySpark developer, data engineer, data analyst, or data scientist for any organisation requires you to be familiar with dataframes because data manipulation is the act of transforming, cleansing, and organising raw data into a format that can be used for analysis and decision making.
For example, you have some user’s data in dataframe-1, and you have to new users’ data in a dataframe-2, then you must find out all the Unmatched records from both dataframe-2 and dataframe-1.
In PySpark, you can retrieve Unmatched rows from two Dataframes using the join, full join and Substract operations.
You have to use some tricks such as —
➽ Matched Dataframe — Use inner join operation combines rows from two Dataframes based on a common column.
