Data migration projects involve transferring data to a new platform while maintaining existing functionalities like data ingestion, transformation, and access for analytics. This process can be complex because the old and new databases often have different architectures, data types, and query processing methods. For example, migrating from a traditional relational database to a distributed database can significantly impact SQL query execution, especially for queries involving JOINs.
Data migration is not simply about copying data. It requires updating ETL pipelines to interact with the new platform and reconfiguring reporting and dashboard tools to connect to the new data source. Thorough testing is crucial to ensure data integrity, validate successful data transfer, and confirm the compatibility of existing tools and processes with the new platform. This includes verifying data consistency, completeness, and the functionality of connected applications.
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What is Data Migration Testing
Data migration testing verifies that all data was correctly moved to a new database and that all data pipelines and client tools work with the new platform. It involves verifying the accurate transfer of data, validating the functionality of data pipelines with the new system, and confirming the compatibility of client tools and applications. This process can be broken down into three key phases:
- Data Migration Testing: This phase focuses on verifying the integrity and completeness of the migrated data. Specialized data quality tools are used to compare data between the old and new databases, ensuring no data is lost, corrupted, or duplicated during the transfer. Tests may include row counts, data profiling, and reconciliation to identify discrepancies.
- Data Pipeline Testing: Existing data pipelines must be adapted to work with the new platform. This requires a two-pronged testing approach. First, data quality tools are employed to compare the outputs of the legacy and new pipelines, ensuring data consistency. Second, a traditional Software Quality Assurance (SQA) approach is used, involving test case design and execution to validate the functionality of the updated pipelines.
- Client Tool Testing: This phase ensures that business applications, BI platforms, and other tools that interact with the data can seamlessly connect and operate with the new platform. A standard SQA approach is crucial, involving requirement gathering from end-users, test case development to cover various functionalities, and thorough testing to confirm correct operation and avoid disruptions to business processes.
What are the Examples of Errors In Data Migration
Data migration projects are susceptible to unique errors that don’t typically occur in greenfield projects. These errors often stem from differences between the old and new database systems, particularly in supported data types and features.
Data Type Mismatches: One common issue is data type incompatibility. For instance, traditional databases like Oracle, SQL Server, or PostgreSQL support the DECIMAL data type for precise storage of numerical values with fixed decimal places (e.g., currency). However, some modern Big Data platforms like Apache Spark or Trino might not offer an exact DECIMAL equivalent. Storing such values in a FLOAT data type on these platforms can lead to a loss of precision.
Manual Errors: Human error during manual tasks like table creation or data migration script implementation can also introduce problems. For example, an engineer might mistakenly choose a DATE data type in the new platform for a column originally storing DATETIME values in the old system. This oversight would result in the loss of the time component.
Transformation Errors: Errors in data transformation code can lead to data corruption or mismatches. These errors can include unintentionally skipping columns during the transfer, incorrectly mapping source and target columns, or applying faulty transformation logic. Such mistakes can result in data being loaded into the wrong columns or being transformed incorrectly.
How To Test Successful Migration
Ensuring a successful data migration requires a rigorous testing strategy. A key aspect of this is data reconciliation, which involves verifying that the data in the new platform matches the data from the original source.
Initial Data Reconciliation
In many migration scenarios, both the old and new platforms operate in parallel for a period. This allows for immediate data comparison after each table is migrated. This initial reconciliation helps to:
- Validate Data Type Mapping: Ensure that data types in the new platform are correctly mapped to those in the old system, preventing data truncation or corruption.
- Verify Column Mapping: Confirm that data is loaded into the correct corresponding columns in the new database.
- Detect Data Loss: Identify any data loss that may have occurred during the transfer process.
Data Reconciliation after Data Pipeline Transition
After migrating the ETL pipelines to the new platform, another round of data reconciliation is paramount. This helps to:
- Uncover Pipeline Errors: Detect errors introduced during the data ingestion, transformation, or loading stages of the new pipelines.
- Ensure Data Consistency: Verify that the new pipelines produce the same output as the old ones, maintaining data integrity.
By performing data reconciliation at these two critical stages, data teams can significantly reduce the risk of data inconsistencies and ensure a smooth and successful migration.
Best Practices for Successful Migration Testing
To ensure a successful and efficient data migration testing process, consider these best practices:
- Data Profiling: Analyze and compare key statistical metrics (e.g., row counts, distinct values, data distributions) between corresponding tables in the old and new systems. Significant differences can highlight potential data quality issues or migration errors.
- Row-Level Data Comparison: For smaller tables with static data (not frequently updated), conduct a row-by-row comparison to identify any discrepancies. This granular approach can pinpoint specific data errors.
- Comparing Large Datasets: For large tables, comparing data row-by-row might be impractical. Instead, group records and compare aggregated values (e.g., sum of sales per day, number of orders per customer). This approach allows for efficient comparison while still identifying potential inconsistencies.
- Performance Testing: Evaluate the performance of the new platform with representative workloads. This includes testing the execution time of critical queries and data processing tasks to ensure the new system meets performance requirements.
- Compliance Validation: Verify that the new platform adheres to all relevant security policies, backup and recovery procedures, and regulations concerning sensitive data. This includes testing access controls, data masking, and data recovery processes.
Data quality best practices - a step-by-step guide to improve data quality
- Learn the best practices in starting and scaling data quality
- Learn how to find and manage data quality issues
What is the DQOps Data Quality Operations Center
DQOps is a data quality and observability platform designed to monitor data and assess the data quality trust score with data quality KPIs. DQOps provides extensive support for configuring data quality checks, applying configuration by data quality policies, detecting anomalies, and managing the data quality incident workflow.
DQOps is a platform that combines the functionality of a data quality platform to perform the data quality assessment of data assets. It is also a complete data observability platform that can monitor data and measure data quality metrics at table level to measure its health scores with data quality KPIs.
You can set up DQOps locally or in your on-premises environment to learn how DQOps can monitor data sources and ensure data quality within a data platform. Follow the DQOps documentation, go through the DQOps getting started guide to learn how to set up DQOps locally, and try it.
You may also be interested in our free eBook, “A step-by-step guide to improve data quality.” The eBook documents our proven process for managing data quality issues and ensuring a high level of data quality over time. This is a great resource to learn about data quality.
