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> data migration for ML and analytics
data migration for ML and analytics
Pre-Migration Planning
Define the scope of the migration project
Identify stakeholders and establish a project team
Assess current data sources and systems
Determine data quality and integrity requirements
Develop a migration timeline and milestones
Data Assessment
Conduct a data inventory
Evaluate data formats and structures
Identify data dependencies and relationships
Analyze data quality issues and remediation needs
Document existing data governance policies
Migration Strategy
Choose a migration approach (big bang, phased, etc.)
Select migration tools and technologies
Plan for data transformation and mapping
Establish data validation and verification methods
Develop a rollback plan in case of issues
Execution
Prepare the environment for migration
Execute data extraction from source systems
Perform data transformation and cleansing
Load data into the target systems
Monitor the migration process for errors
Post-Migration Validation
Validate data integrity and accuracy
Conduct user acceptance testing (UAT)
Review data access and permissions
Ensure compliance with data governance policies
Document any discrepancies and resolutions
Documentation and Training
Update data documentation and metadata
Train users on new systems and processes
Create a support plan for post-migration issues
Share lessons learned with the team
Ensure ongoing monitoring and maintenance of data quality
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