**Data Assessment and Planning:**
– Identify the historical data to be migrated, including data sources, formats, and dependencies.
– Define the scope of the migration and establish migration goals, such as preserving data lineage and auditing trails.
1. **Source Data Extraction:**
– Extract historical data from source systems, databases, or files while preserving timestamps, unique identifiers, and any associated metadata.
2. **Data Transformation and Mapping:**
– Map the source data to the Snowflake schema, considering transformations, data type conversions, and any adjustments required.
– Document the transformation logic for future reference.
3. **Data Validation:**
– Perform thorough data validation and profiling on the extracted and transformed data to ensure its accuracy and completeness.
4. **Create Staging Tables:**
– Create staging tables in Snowflake to temporarily store the historical data during the migration process.
– Staging tables provide a secure location for data transformation, validation, and auditing before loading into final tables.
5. **Data Loading and Transformation:**
– Load historical data into the staging tables using Snowflake’s **`COPY INTO`** command or other loading methods.
– Implement any required transformations, cleansing, and data quality checks within the staging area.
6. **Audit Trail Implementation:**
– Implement audit columns (e.g., creation date, modification date, user ID) in the staging and target tables to track changes.
– Capture additional metadata, such as source system identifiers or data provenance, to maintain proper data lineage.
7. **Data Quality and Lineage Auditing:**
– Perform data quality audits and lineage tracing to validate that the migrated data matches the expected results and adheres to the established data lineage.
8. **Data Transformation and Loading to Final Tables:**
– After staging, transform and load the historical data from the staging tables into the final Snowflake tables using appropriate loading methods.
– Continue to apply data quality checks and audit trail updates during this step.
9. **Audit Logging and Monitoring:**
– Implement logging mechanisms to capture changes, modifications, and updates made to the historical data during the migration process.
– Monitor the migration process and review audit logs for any anomalies or discrepancies.
10. **User Acceptance Testing (UAT):**
– Involve stakeholders in UAT to validate the migrated historical data, data lineage, and auditing records.
– Address any feedback and make necessary adjustments.
11. **Documentation and Communication:**
– Document the entire migration process, including data lineage, transformation rules, and audit trail details.
– Communicate the successful migration and the availability of the historical data in Snowflake to relevant users and teams.
12. **Data Lineage and Auditing Post-Migration:**
– Continue to track and update data lineage and audit information for ongoing data management and compliance.
13. **Backup and Rollback Plan:**
– Develop a comprehensive backup strategy to ensure data recoverability in case of unexpected issues.
– Establish a rollback plan to revert to the previous state in case of critical errors.
By following these steps, you can migrate historical data to Snowflake while maintaining proper data lineage and auditing, ensuring data integrity, traceability, and compliance throughout the migration process and beyond.