Comparison of custom queries

Last modified: July 7, 2023

dbForge Data Compare for SQL Server allows comparing the results of custom queries, rather than comparing entire tables or views. This capability provides a more granular approach to data comparison, allowing users to focus specifically on the query results they are interested in. By comparing custom query results, users can effectively identify differences, similarities, and data inconsistencies, aiding in data validation, troubleshooting, and decision-making processes.

The feature offers several benefits:

Granular analysis: Instead of comparing entire tables or views, focusing on custom query results allows for a more targeted and specific analysis. It enables users to precisely examine the data subsets they are interested in, saving time and effort.

Flexible comparison scenarios: Custom queries can be tailored to match specific business requirements or complex data scenarios. Comparing the results of these queries provides flexibility in evaluating and validating specific data transformations or calculations.

Data validation: Comparing custom queries helps in verifying the accuracy and consistency of data. By comparing the expected results with the actual query output, any discrepancies or anomalies can be promptly identified and addressed.

Troubleshooting: When troubleshooting data-related issues or investigating discrepancies, comparing custom query results can assist in isolating specific data subsets or conditions that may be causing the problem. This focused approach aids in targeted problem-solving and quicker resolution.

Improved decision-making: By comparing the results of custom queries, users can gain valuable insights and make informed decisions based on specific data subsets. This targeted analysis enables a better understanding of data patterns, trends, and relationships, enhancing decision-making processes.

Reduced overhead: Instead of comparing entire tables or views, focusing on custom query results can reduce the overhead associated with data comparison. This efficient approach optimizes resource utilization and streamlines the comparison process.