Data Migration Plan: AI Technology Data Consolidation (Test Run)

This document outlines a comprehensive plan for a data migration project, specifically focusing on consolidating data related to "AI Technology" as a "Test run." The goal is to migrate relevant AI-specific data from a legacy system to a new, modern platform, ensuring data integrity, accuracy, and usability for advanced analytics and AI model development.

1. Project Overview & Scope

Project Title: AI Technology Data Consolidation - Test Run

Description: This test run migration aims to transfer a representative subset of AI-related data (e.g., model metadata, training run logs, dataset references, performance metrics) from an existing legacy database (Source) to a new AI Data Lake / ML Platform (Target). The primary objective is to validate the migration process, field mappings, transformation rules, and rollback procedures before a full-scale migration.

Migration Goal: To establish a robust, repeatable, and validated process for migrating AI technology-related data, ensuring data quality and readiness for the new platform's functionalities.

Scope: Migration of selected tables/entities pertinent to AI models, training runs, and associated datasets. Excludes historical archival data not directly used by the new platform.

2. Data Migration Strategy

For this test run, a Phased Approach is recommended, focusing on a critical subset of data. This allows for iterative testing, validation, and refinement of the migration process with minimal impact.

• Phase 1 (Pre-Migration): Discovery, planning, source data profiling, mapping, rule definition, environment setup.
• Phase 2 (Development & Testing): ETL script development, unit testing, integration testing, performance testing on a staging environment.
• Phase 3 (Test Run Execution): Execute migration for a defined subset of data in a dedicated test environment, followed by comprehensive validation.
• Phase 4 (Post-Migration Review): Analyze test run results, refine processes, document lessons learned.

3. Source and Target Systems

Source System:

• Type: Legacy Relational Database (e.g., MySQL, PostgreSQL, SQL Server)
• Name (Hypothetical): AI_Research_DB_Legacy
• Key Entities (Examples): models, training_runs, datasets, experiments, metrics

Target System:

• Type: Modern AI Data Lake / ML Platform (e.g., Databricks, Snowflake, Azure ML, AWS SageMaker Data Catalog)
• Name (Hypothetical): AI_Platform_DataLake
• Key Entities (Examples): ml_models, training_jobs, data_sources, experiment_logs, model_performance

4. Field Mapping (Example Subset)

Below is an example mapping for a hypothetical models table from the source to ml_models in the target.

| Source Table: models | Target Table: ml_models |

| :---------------------- | :------------------------ |

| Source Field Name | Target Field Name |

| model_id (INT, PK) | model_uuid (UUID, PK) |

| model_name (VARCHAR) | model_name (VARCHAR) |

| algo_type (VARCHAR) | algorithm_type (VARCHAR)|

| version (VARCHAR) | model_version (VARCHAR) |

| owner_id (INT) | owner_user_id (UUID) |

| created_ts (DATETIME) | created_at (TIMESTAMP) |

| last_mod_ts (DATETIME)| last_modified_at (TIMESTAMP) |

| status (VARCHAR) | deployment_status (VARCHAR) |

| accuracy_score (FLOAT)| primary_metric_value (FLOAT) |

| dataset_id (INT, FK) | training_dataset_uuid (UUID, FK) |

| model_path (TEXT) | model_artifact_uri (TEXT) |

| notes (TEXT) | description (TEXT) |

Key Considerations:

• Primary Keys: Often change from auto-increment integers to UUIDs in modern distributed systems.
• Timestamps: Standardize to UTC and consistent formats (e.g., ISO 8601).
• Foreign Keys: Ensure referential integrity is maintained or re-established using new target primary keys.
• Data Types: Align with target system's best practices (e.g., TEXT in source might map to VARCHAR(MAX) or STRING in target).

5. Transformation Rules

Specific rules for data manipulation during the migration process:

1. UUID Generation for Primary Keys:

* Rule: For models.model_id and datasets.dataset_id, generate a new UUID (model_uuid, training_dataset_uuid) in the target system. Store the original model_id / dataset_id as an original_source_id column for traceability.

* Impact: Ensures uniqueness across potentially merged sources and aligns with modern platform practices.

1. User ID Mapping:

* Rule: Map models.owner_id (INT) to ml_models.owner_user_id (UUID) using a lookup table from a central Identity Management system. If no mapping exists, default to a generic "unassigned" UUID.

* Impact: Standardizes user identification across systems.

1. Algorithm Type Standardization:

* Rule: Standardize models.algo_type values (e.g., 'RF', 'GBM', 'XGBoost', 'CNN') to a predefined set of canonical names (e.g., 'Random Forest', 'Gradient Boosting Machine', 'XGBoost', 'Convolutional Neural Network'). Handle variations and potential misspellings.

* Impact: Improves data consistency and queryability in the target.

1. Deployment Status Normalization:

* Rule: Map models.status values (e.g., 'D', 'P', 'R', 'A') to meaningful ml_models.deployment_status values (e.g., 'Draft', 'Pending Review', 'Ready for Deployment', 'Active').

* Impact: Provides clear, human-readable status indicators.

1. Metric Aggregation/Renaming:

* Rule: Rename models.accuracy_score to ml_models.primary_metric_value. If multiple metrics exist in the source, select the primary one for this field, or create additional fields for other metrics.

* Impact: Aligns with target system's metric terminology.

1. Path Conversion:

* Rule: Convert models.model_path (e.g., /legacy/storage/models/v1/model_A.pkl) to a new ml_models.model_artifact_uri (e.g., s3://ai-artifacts-bucket/models/model_A/v1/model.pkl) based on the new storage architecture. This may involve string manipulation and prefixing.

* Impact: Ensures artifacts are correctly referenced in the new environment.

1. Timestamp Conversion:

* Rule: Convert all DATETIME fields (created_ts, last_mod_ts) to UTC TIMESTAMP format (YYYY-MM-DDTHH:MM:SSZ).

* Impact: Ensures time zone consistency and simplifies time-based queries.

1. Description Enrichment:

* Rule: Concatenate models.notes with a standard prefix "Migrated from Legacy AI Research DB: " to form ml_models.description.

* Impact: Provides context for migrated data.

6. Validation Scripts & Procedures

Robust validation is critical to ensure data integrity.

A. Pre-Migration Validation (Source Data Profiling & Quality Checks):

• Tool: SQL queries, Data Profiling tools (e.g., Great Expectations, custom scripts).
• Checks:

* Completeness: Identify NULL values in mandatory fields (model_id, model_name).

* Uniqueness: Verify uniqueness of primary keys (model_id).

* Consistency: Check for consistent data formats (e.g., algo_type values).

* Referential Integrity: Verify foreign key relationships (dataset_id exists in datasets table).

* Volume: Record total row counts for each source table.

B. Post-Migration Validation (Target Data Integrity & Accuracy Checks):

• Tool: SQL queries, Spark/Python scripts, data comparison tools.
• Checks:

1. Record Count Verification:

Script: SELECT COUNT() FROM source.models; vs. SELECT COUNT(*) FROM target.ml_models;

* Expected: Counts should match for the migrated subset.

2. Key Field Uniqueness:

Script: SELECT model_uuid, COUNT() FROM target.ml_models GROUP BY model_uuid HAVING COUNT(*) > 1;

* Expected: Returns no rows.

3. Data Type Verification:

* Script: Sample data from target and verify data types match schema (e.g., primary_metric_value is FLOAT).

4. Referential Integrity Check:

* Script: Verify training_dataset_uuid in ml_models correctly links to data_sources.dataset_uuid.

* Expected: All foreign keys should resolve.

5. Sample Data Comparison:

* Script: Randomly select 5-10 records from the source, transform them manually (or using a test script), and compare with the corresponding migrated records in the target. Focus on complex transformations.

* Expected: Transformed data in target matches expected output.

6. Business Rule Validation:

* Script: Verify specific business rules (e.g., ml_models.deployment_status must be one of the canonical values; primary_metric_value must be between 0 and 1 for accuracy scores).

* Expected: All migrated data conforms to new business rules.

7. Performance Check: Query target tables to ensure acceptable query performance.

7. Rollback Procedures

A robust rollback plan is essential for mitigating risks during a test run or actual migration.

1. Pre-Migration Backups:

* Source System: Perform a full logical and physical backup of the AI_Research_DB_Legacy before initiating any migration activities.

* Target System: If the target system is not empty, ensure a snapshot or backup of the AI_Platform_DataLake state immediately prior to the migration attempt.

1. Transaction Management:

* Strategy: For the test run, perform migrations within a transaction block if the target system supports it, allowing for an immediate ROLLBACK if issues are detected during the migration process itself. If not, use a "truncate and reload" strategy for the test subset.

1. Rollback Steps (if issues detected post-migration):

* Step 1: Halt Migration Process: Immediately stop any ongoing migration jobs.

* Step 2: Isolate Target Data: If the target system was not empty, identify and delete only the data inserted/updated by the failed migration batch using audit logs or the original_source_id field.

* Step 3: Restore Target State: If isolating is too complex or risky for the test run, restore the AI_Platform_DataLake tables/entities relevant to the migration from the pre-migration backup/snapshot.

* Step 4: Verify Rollback: Confirm that the target system has reverted to its pre-migration state.

* Step 5: Incident Communication: Inform all stakeholders about the rollback and the reasons for it.

* Step 6: Post-Mortem & Remediation: Analyze the root cause of the failure, update migration scripts/plan, and re-test.

1. Contingency Plan:

* Manual Data Entry: In extreme cases where automated rollback fails for a small test subset, be prepared for manual data correction or re-entry for critical records, though this should be avoided.

* Temporary Data Freeze: Implement a temporary freeze on data updates in the source system if issues are critical and require extended investigation.

8. Timeline Estimates (Test Run Focus)

This timeline is a high-level estimate for a test run of a medium complexity migration, assuming dedicated resources.

| Phase | Estimated Duration (Test Run) | Key Activities |

| :-------------------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |

| 1. Planning & Analysis | 3 Days | Project scope finalization, stakeholder alignment, source data profiling, field mapping, transformation rule definition, rollback strategy. |

| 2. Development | 5 Days | ETL script coding (extraction, transformation, loading), unit testing of individual components. |

| 3. Testing & QA | 4 Days | Integration testing, data validation script development, performance testing on test dataset, bug fixing. |

| 4. Test Run Execution | 1 Day | Execute migration scripts on a representative test subset, real-time monitoring. |

| 5. Post-Migration Review| 2 Days | Comprehensive validation, error analysis, performance review, documentation updates, lessons learned. |

| Total Estimated Time | 15 Working Days | |

Note: This timeline is for the test run only. A full-scale migration would require significantly more time for larger data volumes, complex transformations, and broader stakeholder coordination. Buffer days should be added for unforeseen issues.

9. Key Risks & Mitigation

| Risk | Mitigation Strategy |

| :--------------------------------- | :-------------------------------------------------------------------------------------------------------------- |

| Data Loss/Corruption | Comprehensive backups, transaction management, robust validation scripts, phased migration, small test subsets. |

| Inaccurate/Incomplete Mapping | Thorough data profiling, iterative review of mappings with SMEs, automated schema comparison tools. |

| Performance Bottlenecks | Load testing, optimize ETL scripts, incremental migration strategy, scale target system resources. |

| Downtime Impact | Schedule migration during off-peak hours, communicate downtime clearly, utilize near-zero downtime techniques. |

| Scope Creep | Clearly defined scope document, strict change control process, regular stakeholder communication. |

| Skill Gaps in Team | Provide training, engage experienced consultants, leverage automated migration tools. |

| Rollback Failure | Test rollback procedures in isolation, ensure multiple backup strategies, detailed rollback documentation. |

10. Recommendations & Next Steps

Recommendations:

1. Start Small: Prioritize a small, non-critical but representative dataset for the initial test run to minimize risk and gather early feedback.
2. Automate Everything: Automate ETL processes, validation scripts, and even rollback steps where possible to reduce manual error and increase repeatability.
3. Involve Stakeholders Early: Engage data owners, AI engineers, and business users from the start to validate mappings and transformation rules.
4. Document Thoroughly: Maintain detailed documentation of source system analysis, field mappings, transformation rules, and all migration scripts.
5. Monitor Closely: Implement robust monitoring during the migration execution to detect issues in real-time.

Next Steps:

1. Detailed Source System Analysis: Conduct a deep dive into AI_Research_DB_Legacy schema, data types, constraints, and data quality.
2. Finalize Field Mapping: Complete the comprehensive field mapping for all in-scope tables, including data types and nullability constraints.
3. Develop ETL & Validation Scripts: Begin development of the extraction, transformation, loading (ETL) scripts and corresponding validation checks.
4. Environment Setup: Ensure the dedicated test migration environment (source and target) is provisioned and configured.
5. Kick-off Meeting: Schedule a formal kick-off meeting with all relevant stakeholders to review and approve this plan.