Preparing for the Microsoft Fabric Certification (DP-600) exam? Mastering data manipulation in Spark is essential, and this post dives into two key techniques: merging and deduplicating data using PySpark, SQL, and Dataflow Gen2.
Data Merging:
PySpark: Utilize `union` for merging dataframes with identical schemas and `unionByName` to handle scenarios with missing columns, leveraging the `allowMissingColumns` parameter for flexibility.
SQL: Employ `UNION` to combine datasets while removing duplicates and `UNION ALL` to preserve all rows. Address schema mismatches by using `NULL` for missing values.
Dataflow Gen2: Visually merge data using the "Append" transformation, choosing between Union and Union All based on your duplicate handling requirements.
Data Deduplication:
Identifying Duplicates: In PySpark, combine `groupBy`, `count`, and `where` to pinpoint duplicate rows. In SQL, leverage `GROUP BY` and `HAVING` for the same purpose.
Removing Duplicates: Utilize `distinct` in both PySpark and SQL to eliminate entire duplicate rows. In PySpark, `dropDuplicates` provides granular control by removing duplicates based on specific columns. Dataflow Gen2 offers a visual "Remove Rows" transformation for deduplication.
#Azure #DataEngineering #DP600 #Spark #PySpark #SQL #DataflowGen2 #DataManipulation #DataMerging #DataDeduplication
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