Data Reconciliation With Spark SQL

Data reconciliation is the process of comparing and validating data from different sources to ensure consistency, correctness, and completeness. In Spark, this is commonly used in ETL pipelines, data warehouses, and data lake architectures.

Row-Level Comparison

Identify missing, extra, or mismatched records.

All above methods for row comparison requires shuffle which is expensive operation.

• To optimized it hashing can be used

✅ Left anti join on hash column minimizes data movement compared to direct joins on all columns.

✅ Avoids shuffle – Since hashing operates on each row independently, no data movement is needed.

✅ Efficient – Reduces large Data Frame comparisons to single-column hash comparisons.

✅ Scalable – Works well for big data where row-by-row comparison is too expensive.

✅ Minimal Memory Overhead – Hash values are much smaller than full row data.

Below additional 2 methods help to avoid shuffle under certain conditions

Other Comparisons

• Schema comparison – This only compares metadata and does not require any data movement.. df1.schema != df2.schema
• Aggregated Metrics Comparison – Compare counts, sums, averages, etc. If full row-by-row comparison is too expensive, compare aggregates instead. No Shuffle Needed (Efficient for large datasets)

Handling Duplicates

• Identify duplicate or inconsistent data. Minimal Shuffle (Better than full row comparison)