What is data lifecycle management?

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KEY TAKEAWAYS

The data lifecycle.

Unstructured data moves through its stages without announcing it, so the stage has to be read from the file’s own attributes in a metadata catalog.

What is data lifecycle management?

Data lifecycle management is the practice of deciding what happens to data at each stage of its life and applying those decisions as rules rather than as projects. It covers where data sits, when it moves, how long it is kept, and what proves it was treated correctly.

What are the stages of the data lifecycle?

Creation, active use, cooling, archive, and retirement. Unstructured data moves through these stages without announcing it, so you have to derive a file’s stage from its attributes. Last access date is the strongest single signal, followed by project status and owner.

Data tiering.

Tiering is placement matched to how often a file is read, and the savings it produces is the subject of storage cost optimization.

What is data tiering?

Data tiering is placing each file on storage matched to how often it is read. Frequently read data sits on the fastest tier, rarely read data on slower and less costly storage, and data kept for the record on archive storage. The decision is economic, and it depends on knowing access patterns.

What is the difference between file-level and block-level tiering?

Block-level tiering moves blocks within a storage system and is invisible to users and to any catalog. File-level tiering moves whole files between systems and tiers, so each move is visible, attributable, and reversible. Only file-level tiering can be driven by attributes such as project, owner, or retention class.

What is cloud tiering?

Cloud tiering moves cold data from on-premises storage to cloud storage classes priced for infrequent access. The savings are real, and the risk is read cost, because cloud providers charge more to read data out than to put it in. The decision depends on access history, not on capacity.

What is archival storage, and when should data be archived?

Archival storage is low-cost storage for data that must be kept and is rarely read. The right moment to archive is when a file has passed its active life and still has a retention obligation or a plausible future use. Deciding that from evidence rather than instinct is the entire difficulty.

Data mobility and migration.

Movement is also when governance risk gets created, because a file can land somewhere its retention class does not allow. Our blog post on what data automation actually means covers how policy keeps that from happening.

What is data mobility?

Data mobility is the ability to move files between storage systems, tiers, sites, and clouds on policy rather than as a project. It differs from a migration in that it is continuous and rule-driven. A catalog makes it safe because it decides which files qualify.

What is the difference between data mobility and data migration?

A migration is a one-time move, usually driven by a hardware refresh or a cloud adoption, with a start and an end. Mobility is an ongoing capability where files move whenever a rule says they should. A migration is a project. Mobility is an operating state.

What is data repatriation?

Data repatriation is moving data and workloads from public cloud back to on-premises or colocation storage. It usually follows a cost review where read and egress charges turned out to be higher than modeled. The same catalog that justified the move out is what makes the move back measurable.

Dry runs and safe moves.

Every rule previews before it runs. The named list a dry run produces is the same evidence unstructured data governance asks for after the fact.

How does a catalog decide what to move?

It applies rules to attributes it already holds. A typical rule combines age, last access date, path or project, owner, and format, then produces a list of matching files. The rule is written once and keeps running, which is what distinguishes it from an annual clean-up.

What is a dry run, and why does it matter before a move?

A dry run reports exactly which files a rule would affect, without moving anything. It turns a policy from a risk into a reviewable proposal, and it lets someone approve a change covering millions of files with a clear conscience.

What happens to the catalog when a file moves?

The catalog record follows the file. That is what keeps a move from breaking the estate view: the file is still described, still attributable to its project and owner, and still findable, with its new location recorded. A move that loses the description has traded one visibility problem for another.