Data Migration¶

Transfer a defined dataset to a target system and hand its authoritative use to that target under a suitable fidelity criterion.

Version: v1 · 2026-10-03 · History
Domain-specific #: 13124
Domain group: Applied Sciences & Engineering
Origin domain: Computer Science & Software Engineering
Subdomains: Data Management, Systems Migration → Computer Science & Software Engineering

Core Idea¶

Data migration moves a defined body of digital data from a source to a target system that succeeds it as the serving location for the same defined workload. Transfer, suitable evidence that the target data is faithful, and handover of use are the recurring roles. Copying alone is not enough. Schema conversion, deduplication, immediate source deletion and a single irreversible cutover are optional rather than defining.[^{ref-cf8f32e0cd02][}ref-9883f488380d]

Scope of Application¶

The pattern appears in storage-array replacement, database-platform change and cloud-bucket moves. IBM's DS8870 storage case copies or mirrors volumes and switches applications to target volumes without requiring a schema rewrite. AWS DMS can load source tables, capture later changes, check corresponding rows and repoint applications to a target database. Google Cloud distinguishes the target-use step from later source deletion.[^{ref-9883f488380d][}ref-cf8f32e0cd02][^{ref-97ae3e2cb41f][}ref-d15f955e6236]

Clarity¶

Three milestones differ: a copy exists, the data meets a stated fidelity criterion, and the target now serves the previously source-served workload. A replica or backup may meet the first two while leaving the original in charge. An ETL feed may repeatedly populate a new analytics destination without taking over the original operational workload. Conversely, migration can occur with unchanged data layout. The choice of tool does not settle the identity; the change of serving authority for the same use does.[^{ref-cf8f32e0cd02][}ref-9883f488380d][^ref-d15f955e6236]

Manages Complexity¶

Many tables, volumes, object versions, mappings and in-flight updates can be organized around five questions: what data is in scope, where is it now, what target should serve it, how is faithful correspondence checked, and when does use shift? Fidelity checks differ: AWS row comparisons identify mismatches and suspended validation records, while IBM storage guidance looks for synchronized volume state before an application switch. One check does not prove every application-level property.[^{ref-97ae3e2cb41f][}ref-9883f488380d]

Abstract Reasoning¶

If source writes continue while the initial copy runs, the target can lag; the reasoning question is what catches it up before handover. Keeping the source active reduces interruption but makes a coherent final state harder to establish. More extensive validation strengthens evidence but uses time and system resources. Ask whether the target has both the needed data and the intended authority before calling the migration complete; retaining the old source for a rollback window does not by itself undo the handover.[^{ref-cf8f32e0cd02][}ref-97ae3e2cb41f][^ref-f7177302e918]

Knowledge Transfer¶

The source–target–transfer–fidelity–handover structure transfers literally between a storage volume and a database, though the mapping and verification methods differ. It is not live Process Migration, which relocates an executing operating-system process, and its portable outline is not by itself a proved prime. No strict DAG parent is asserted in this staged draft.[^{ref-9883f488380d][}ref-cf8f32e0cd02]

[^ref-cf8f32e0cd02]: Amazon Web Services, “High-level view of AWS DMS”, original AWS Database Migration Service documentation, inspected 2026-10-01. [^ref-97ae3e2cb41f]: Amazon Web Services, “AWS DMS data validation”, original product documentation, inspected 2026-10-01. [^ref-9883f488380d]: IBM, “Migrating data between storage systems”, DS8870 7.3.0 original documentation, inspected 2026-10-01. [^ref-f7177302e918]: IBM, “Migration flow and concepts”, IBM i 7.4 original documentation, inspected 2026-10-01. [^ref-d15f955e6236]: Google Cloud, “Transfer between Cloud Storage buckets”, original documentation, inspected 2026-10-01.

Neighborhood in Abstraction Space¶

Data Migration sits in a sparse region of the domain-specific corpus (62^nd percentile for distinctiveness): few abstractions share its structure, so a faithful description tends to retrieve it precisely.

Family — Program Execution & Runtime Concepts (27 abstractions)

Nearest neighbors

Computed from structural-signature embeddings · 2026-10-08