Data Migration Validation vs Data Observability

The Shiny Allure of Data Observability: Its Limits in Data Migration, Integrity Audits, and Certification

Data migration is a critical process for organizations looking to upgrade systems, move to the cloud, or consolidate data platforms. Ensuring that data is accurately transferred, transformed, and verified during this process is crucial to maintaining business continuity and compliance. However, the process doesn't end there. Continuous data integrity audits and certifications are necessary to ensure that data remains accurate, consistent, and reliable over time.

With the rise of data observability as a "new and shiny" tool in the data management space, many data governance and data quality teams are attempting to force-fit these tools to handle not only data migration validation but also ongoing data integrity audits and data certification. However, while data observability tools offer significant benefits for real-time monitoring and pipeline health, they are not designed to fully meet the comprehensive requirements of data migration validation, ongoing data integrity audits, and data certification.

In this extended blog, we'll explore the reasons why data migration validation, ongoing data integrity audit, and data certification requirements cannot be fully satisfied by data observability tools, and why specialized tools and processes are necessary.

1. Scope of Validation vs. Observability

Data migration validation involves verifying that data has been transferred accurately from the source system to the target system. This includes checking data completeness, correctness, consistency, transformations, and adherence to business rules. Typically, this validation is relevant only during the migration project and ensures that the migration has been executed correctly. Once the migration is complete, this validation process generally concludes.

In contrast, ongoing data integrity audits are concerned with continuously validating data to ensure it remains accurate and consistent over time. This is essential for ongoing data reliability monitoring, where data integrity must be maintained as it is accessed, modified, and used across various applications and systems.

Data observability tools focus on monitoring data pipelines in production, detecting anomalies, and ensuring data freshness and quality. They do not typically perform detailed, end-to-end validations across multiple systems, especially when migrating from one environment to another, such as from a legacy system to the cloud. Additionally, they are not equipped to handle continuous auditing of data integrity over time, which is critical for compliance and business operations.

2. Schema and Metadata Differences

Migrations often involve differences in schema and data structure between the source and target systems. Validating a migration requires comparing these schema differences and ensuring that data transformations are correctly applied. This is a one-time task that is essential during the migration process but not typically needed afterward.

However, ongoing data integrity audits must account for schema changes over time and verify that these changes do not compromise data integrity. As systems evolve, schemas can change, and ongoing audits must ensure that these changes do not introduce errors or inconsistencies in the data.

Data observability tools are usually designed to monitor a single system's health and may not handle schema evolution or structural comparisons between different systems effectively. They also lack the capability to continuously validate schema changes against historical data, which is essential for maintaining data integrity over time.

3. Data Transformation Validation

During migration, data often undergoes complex transformations such as data type conversions, formatting changes, or the application of business rules. Validating these transformations requires detailed, rule-based comparisons of pre- and post-migration data to ensure that the business logic is correctly applied.

This validation is crucial during the migration project to ensure that the transformation rules have been correctly applied. Ongoing data integrity audits also need to verify that these transformations continue to produce accurate and consistent data as the system evolves. Changes in business rules or system updates can impact how data is processed, and ongoing audits must ensure that these changes do not introduce errors.

Observability tools generally do not support deep data transformation checks or provide the granular, field-level validations needed for complex migrations or continuous audits. They focus more on identifying real-time issues rather than verifying that complex transformations have been correctly applied and maintained over time.

4. Historical Data Integrity Checks

Validating the integrity of historical data is crucial in migrations, especially those involving large volumes of records. This process involves comparing millions or even billions of records to ensure that the historical data matches the original data in terms of accuracy, timeliness, and structure. This is typically a one-time validation process during migration.

Ongoing audits, on the other hand, must ensure that historical data remains accurate and unaltered as it is accessed and potentially modified over time. This ongoing verification is essential for ensuring long-term data reliability and compliance.

Data observability tools, which focus on monitoring real-time data flows, are not typically equipped to handle historical comparisons or validate large datasets during a migration. They are also not designed to continuously audit historical data for accuracy and integrity, which is essential for compliance and long-term data governance.

5. Audit Trail and Compliance

Data migration often requires generating audit reports to prove compliance with legal or regulatory requirements such as GDPR or HIPAA. These audits must capture the full lineage of data, document transformation steps, and provide a complete record of the migration process. This audit is generally relevant during and immediately after the migration project to ensure compliance with regulations.

Ongoing data integrity audits and certifications, however, require a continuous audit trail to ensure that data remains compliant over time. This is essential for organizations that must regularly demonstrate compliance with regulations and internal standards.

While observability tools are effective at monitoring operational metrics, such as uptime and error rates, they often do not generate the comprehensive audit reports required for regulatory compliance during a migration. They also lack the capability to maintain ongoing audit trails for continuous compliance monitoring.

6. Source-to-Target Comparison

A crucial aspect of data migration is performing source-to-target comparisons to ensure that data has been migrated without loss, duplication, or corruption. This involves directly comparing records in the source system with those in the target system. This comparison is typically conducted during the migration project and is essential for ensuring the accuracy of the migration.

Ongoing data integrity audits must continue to compare data across systems to ensure that no errors are introduced over time. This is particularly important for systems that integrate data from multiple sources or where data is regularly updated or transformed.

Observability tools typically do not support direct comparisons between two systems. They focus on detecting data quality issues within a single system rather than validating data accuracy across different environments.

7. Granular Control and Verification

Data migration validation requires granular control over the process, including the ability to define specific validation rules for different datasets, columns, and data types. It also often involves reconciling records down to the row or column level. This level of control is critical during the migration project to ensure that data is accurately transferred and transformed.

Ongoing data integrity audits also require granular control to continuously verify data accuracy and consistency. As data is accessed, modified, and used across various applications, it is essential to have detailed verification processes in place.

Observability tools generally lack the ability to apply custom validation rules or verify data at such a granular level, as their focus is on monitoring pipeline health rather than validating individual data records.

8. Reconciliation of Business Rules

Many migrations involve applying business rules that affect how data is handled, filtered, or transformed during the migration. Validating these rules ensures they are applied correctly and consistently across all data points. This validation is necessary during the migration project to ensure that business logic is correctly implemented.

Ongoing audits must continue to validate business rules to ensure that they are consistently applied as the system evolves. Changes in business rules or system updates can impact how data is processed, and ongoing verification is essential to maintain data integrity.

Observability tools, however, often do not support business rule validation or reconciliation. They may detect anomalies but will not check whether specific business logic was correctly applied during the migration or over time.

9. Specialized Data Types and Formats

Some data migrations involve specialized data types, including binary data, XML/JSON structures, or proprietary file formats. Validating that these data types are correctly migrated and transformed requires tools specifically designed to understand and process them. This validation is critical during the migration project to ensure that all data types are correctly handled.

Ongoing data integrity audits must also ensure that these specialized data types continue to be processed accurately as the system evolves. Changes in data formats or processing rules can introduce errors, and continuous verification is necessary to prevent these issues.

Observability tools generally monitor standard data formats and may not have the capability to validate or interpret specialized data formats or custom file structures. They are also not equipped to continuously audit these data types for accuracy and consistency.

10. Batch vs. Real-Time Data Processing

Data migration is often a batch process, involving the transfer and validation of large volumes of data over time. This process is generally relevant only during the migration project.

Ongoing data integrity audits, however, must account for both batch and real-time data processing. As data is continuously processed and updated, it is essential to ensure that both batch and real-time data remain accurate and consistent.

Observability tools, which are designed for monitoring real-time data pipelines, may not be optimized for batch validation of large datasets—something that is common in migration scenarios. They are also not equipped to handle the ongoing verification of both batch and real-time data.

11. Error Reporting and Resolution

During migration validation, detailed error reports are essential for troubleshooting issues before the migration is finalized. These reports specify what went wrong, such as missing data, format issues, or failed transformations. This level of detailed reporting is typically relevant during the migration project.

Ongoing data integrity audits also require detailed error reporting to identify and resolve issues as they arise. Continuous monitoring and error resolution are essential for maintaining data integrity over time.

Observability tools may identify general issues, such as pipeline failures, but often do not generate the migration-specific insights or ongoing error reports needed for resolving data integrity issues.

Conclusion

While data observability tools are invaluable for monitoring and maintaining the health of data pipelines in real-time production environments, they are not designed to meet the specific requirements of data migration validation, ongoing data integrity audits, and data certification. Many data governance and data quality teams, drawn to the promise of these new and shiny tools, are attempting to force fit them into roles they were not designed to fulfill—such as data migration validation, integrity auditing, and data certification.

However, these tasks demand specialized validation tools that focus on ensuring data accuracy, integrity, and compliance across different systems, data types, and business rules—capabilities that go beyond the scope of observability solutions. For organizations undergoing data migration and looking to maintain ongoing data reliability, it's essential to deploy the right tools and processes to ensure a successful transition and ongoing data quality. By understanding the limitations of data observability tools in this context, businesses can better prepare for the complexities of data migration and ongoing data governance, thereby avoiding costly errors and ensuring long-term success.

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