DATA MIGRATION METHODOLOGY
One of the most important commodities that a business controls, owns, or maintains is its ‘data’ and therefore, any data migration is a high-risk activity. As a result, it must undergo significant verification and validation efforts.
EXERCISING DATA MIGRATION TASK
The core of exercising a data migration task is to perform an (ETL) ‘Extract, Transform, and Load’ operation that extracts the data from various sources, fixes/transforms it, and then loads it into the target data warehouses. This offers our testing team some of the key consideration points to help them effectively plan tests and strategize accordingly for data migration activities
Data Migration Testing Phases
Review the design document or detailed requirements
Understand and define the source and scope of data (Extraction Process)
Understand and define the target data-warehouse (Load Process)
Understand the data scheme including mandatory fields, field names, field types, data types, etc. for both the original data source and the destination system
Understand the data cleaning requirements
Understand any interfacing with other systems
Once done with the above steps, we:
- Correct mapping to the user interface
- Correct mapping to the business process
- Configure the software testing tool
- Correct scripts generation using the field details mentioned above.
- Identify sample subset data (review with the client to get approval and minimize the risks)
Post Migration Testing
After completing the migration activity, we perform black box testing/acceptance testing against business cases, use cases, and user stories. We use the same data subsets for post-migration testing that we used during the pre-migration testing. We perform this activity in a test environment, not the production one.
User Acceptance Testing
We perform user acceptance testing after the post-migration testing phase as the functional fragilities as a result of the consolidation of the migrated data may be difficult to point out in the previous phases. We do this to interact with the legacy data in the destination system before we release it to the production environment.
Production Migration Testing
We understand that the execution of the above phases successfully
does not guarantee that the production process will be errorless.
Some of the challenges/areas that we take care of at this stage are.
Data Quality Tools Following are some of the areas that DQ tools can help with,
- Cost and Scope Analysis
- Migration Simulation
- Data Quality Rules Management
- Migration Execution Sequencing
- Independent Data Migration Validation on Target System
- Ongoing Data Quality Assurance
Tools Following are some of the tools for data migration activity:
- Adeptia
- Actian
- Attunity
- Denodo
- Microsoft SSIS
- Oracle Data Integrator
- SAP – Data Services
- TIBCO – Data Virtualization
- Hitachi Vantara – Pantaho Data Integrations
- Informatica – PowerCenter
- IBM – InfoSphere DataStage
- SAS
- Syncsort – DMX-h
- CloverETL
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