🚚Data Migration

NEW

Permanently moving data from one storage system or environment to another with minimal disruption and verified integrity.

DESCRIPTION

Data migration moves datasets between systems, versions, or platforms—often to modernize, consolidate, or scale. It involves planning, profiling, scoping, and mapping before extraction, transformation, and load. Strategies include big-bang cutover or phased/coexistence approaches. Critical success factors are data quality remediation, test runs, and rollback plans. Security, privacy, and regulatory compliance must be preserved throughout. Downtime windows and throughput constraints drive technical choices. Validation ensures completeness, accuracy, and reconciliation against sources. Lineage documents what moved, when, and how. Automation reduces cost and risk. Well-run migrations create maintainable, governed target environments.

RELATED TERMS

Extract-Transform-Load Schema Migration Cutover Strategy Data Conversion Data Validation Lineage Lift and Shift Governance Data Quality Data Replication

COMPETING TERMS

Data Synchronization

RELATIONS

• Broader: Migration

KNOWLEDGE AREAS

Data Management Data Integration & Interoperability Cloud Data Architecture

TARGET AUDIENCE

Data Architect Data Engineer Cloud Architect Data Governance Manager Data Management Consultant

EXTERNAL SOURCES

• Wikipedia - Data migration
• AWS - AWS Database Migration Service documentation
• Microsoft - Cloud Adoption Framework migration guidance
• Google Cloud - BigQuery analytics migration overview
• IBM - Overview of data migration concepts

TOP QUESTIONS

What are the main approaches to Data Migration?

Common approaches are big-bang (single cutover) and phased (iterative or parallel run). Selection depends on risk tolerance, downtime windows, and complexity.

Category: Overview

Why is Data Migration risky?

Data loss, downtime, and integrity issues can disrupt operations. Proper planning, testing, and rollback strategies mitigate risks.

Category: Risks

How do you ensure Data Migration quality?

Use profiling, cleansing, reconciliation, and sampling. Establish acceptance criteria and run dress rehearsals before production cutover.

Category: Quality

What is the role of Data Mapping in Data Migration?

Mappings define how source fields transform into target schemas, preventing misalignment and ensuring business meaning is preserved.

Category: Relationship

When should you opt for Data Synchronization instead of migration?

Choose synchronization for ongoing bi-directional data exchange, such as hybrid deployments. Migration is typically one-time with final cutover.

Category: Decision

How do you plan a Data Migration cutover?

Define downtime windows, backout plans, communication, and checkpoints. Perform a final delta load and validation before switching traffic.

Category: Management

What tools support Data Migration?

Cloud migration services (AWS DMS, Database Migration Service, BigQuery Transfer), ETL platforms, and orchestration tools support end-to-end workflows.

Category: Tooling

How do compliance requirements affect Data Migration?

Retention, residency, and masking rules must be enforced during transport and in the target. Update records of processing activities accordingly.

Category: Compliance

What is a common misconception about Data Migration?

That it’s a lift-and-shift exercise; in reality, schema and process changes often require transformation, validation, and governance updates.

Category: Misunderstanding