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What is the best way to test data consistency during database upgrades?

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Database upgrades are essential for improving performance, security, and functionality, but they also pose a risk of data inconsistency. Data inconsistency occurs when the data in different tables, databases, or systems do not match or follow the same rules. This can lead to errors, confusion, and loss of trust in the data quality. Therefore, testing data consistency during database upgrades is crucial for ensuring data integrity and reliability. In this article, you will learn what is the best way to test data consistency during database upgrades, and what tools and techniques you can use to achieve it.

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Jo?o Sá

System, Network and Software Engineer | Teacher | Teacher Trainer | Instructor
Michael C.

Database Developer at MSI

1 Why test data consistency?

Testing data consistency is important for several reasons. First, it helps you verify that the upgrade process did not introduce any errors or corruption in the data. Second, it helps you identify and resolve any data discrepancies that existed before the upgrade. Third, it helps you ensure that the data conforms to the new schema, rules, and standards that the upgrade may have implemented. Fourth, it helps you avoid any negative impacts on the data consumers, such as reports, dashboards, applications, or users.

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2 How to test data consistency?

The best way to test data consistency during database upgrades is to use a combination of automated and manual methods. Automated methods include using scripts, tools, or frameworks that compare the data before and after the upgrade, and check for any differences, anomalies, or violations. Manual methods include using queries, reports, or visualizations that inspect the data and verify its accuracy, completeness, and validity. The choice of methods depends on the size, complexity, and type of the data, as well as the resources and time available.

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Jo?o Sá

System, Network and Software Engineer | Teacher | Teacher Trainer | Instructor
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Assuming the database is not in use during the migration, performing a select count(*) on each table, comparing the previous values with the current values, is one of the minimal and elementary actions that can be performed and easily automated using scripts. Manually checking data by sampling between tables can be useful. Randomly compare examples of older records, intermediate records and recent records between tables. In the case of PostgreSQL databases, the pg_upgrade utility is a good helper.

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3 What to test for data consistency?

When testing for data consistency during database upgrades, you must consider schema consistency, content consistency, rule consistency, and quality consistency. Schema consistency involves making sure the structure and definition of the data, such as tables, columns, keys, indexes, constraints, and data types, is consistent across all databases or systems. Content consistency requires that the values and records of the data are consistent across all databases or systems; there should be no duplicates, missing, or invalid data. Rule consistency refers to ensuring that the business rules and logic governing the data are consistent across all databases or systems. Lastly, quality consistency means that the data meets the quality standards and expectations of the data consumers in terms of accuracy, completeness, validity, timeliness, and reliability.

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Jo?o Sá

System, Network and Software Engineer | Teacher | Teacher Trainer | Instructor
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It would be useful to get an automatically generated schema of the old database and the new one and compare them to check relationships and that the overall structure is consistent. In MySQL, the MySQL Workbench does this automatically in seconds. Tools like DataGrip, DbVisualizer, DBeaver or TablePlus are interesting for use with different database engines.

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4 What tools to use for data consistency testing?

Data consistency testing can be done using a variety of tools, from simple and free to sophisticated and paid. Commonly used tools include SQL queries, data comparison tools such as SQL Compare, SQL Data Compare, and dbForge Data Compare, as well as data quality tools like Talend Data Quality, Informatica Data Quality, and Trifacta. These tools can be used to compare the data before and after an upgrade, generate reports or scripts to highlight differences, synchronize the data, assess and improve the quality of the data, and detect and fix any issues or anomalies.

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Michael C.

Database Developer at MSI
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Redgate SQL Data Compare is a very simple time saving tool that allows you to compare data at a granular level. You can compare the entire database at once, or just one table at a time.

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5 What best practices to follow for data consistency testing?

To ensure effective and efficient data consistency testing during database upgrades, you should plan ahead and define the scope, objectives, criteria, and methods of the testing. Additionally, you should backup and restore the data before and after the upgrade, as well as test iteratively in multiple iterations and different scenarios. After the testing is finished, review the results and report any findings to relevant stakeholders. Lastly, document the testing process and outcomes, and archive the test data and artifacts.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What is the best way to test data consistency during database upgrades?

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1 Why test data consistency?

2 How to test data consistency?

3 What to test for data consistency?

4 What tools to use for data consistency testing?

5 What best practices to follow for data consistency testing?

6 Here’s what else to consider

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