Data Management Glossary
Archival Storage
What is Archival Storage?
Archival Storage is a source for data that is not needed for an organization’s everyday operations, but may have to be accessed occasionally. With archival storage, organizations can leverage secondary sources while still maintaining protection of the data. It allows organizations to reduce primary storage costs and maintain data that may be required for regulatory or other requirements.
Data archiving, also known as data tiering, is intended to protect older information that is not needed for everyday operations but may have to be accessed occasionally. Data archival and tiering storage is a tool for reducing primary storage needs and the related costs, rather than acting as a data recovery tool.
How does archival storage work in practice, and how is it different from backup or data recovery?
Archival storage is designed for data that is not required for daily operations but may need to be accessed occasionally. It protects older information while reducing primary storage requirements and associated costs. Data archiving, or data tiering, moves this less active data to secondary storage sources. Unlike backup or data recovery solutions, archival storage is focused on cost reduction and long-term retention rather than rapid recovery after data loss.
Why do organizations use archival storage to reduce primary storage costs and meet regulatory requirements?
The primary benefit of data archiving is that it reduces the cost of primary storage. Archive storage typically costs less because it is based on low-performance, high-capacity storage media. By moving inactive data off expensive primary systems, organizations can maintain data required for regulatory or other requirements without expanding high-cost storage infrastructure. Some data archives allow data to be read-only to protect it from modification, while other data archiving products allow users to modify archived data, depending on business needs.
What storage options are available for archival storage, including disk, object, tape, and cloud?
Data archiving takes several forms. One option is online data storage, where archive data is placed onto disk systems and remains readily accessible. Archives are frequently file-based, though object storage is growing in popularity. A key challenge when using object storage to archive file-based data is the impact on users and applications. To avoid changing paradigms from file to object and breaking user and application access, organizations use data management solutions that provide a file interface to data that is archived as objects.
Another archival system uses offline data storage, where archive data is written to tape or other removable media using data archiving software rather than being kept online. Data archiving on tape consumes less power than disk systems, resulting in lower costs.
A third option is cloud data storage, such as services offered by Amazon and Microsoft Azure. Cloud archival storage can be less expensive if implemented correctly but requires ongoing investment. A Smart Data Migration strategy is essential to control long-term costs and ensure efficient data movement.
How does the data archiving process automate movement of cold data while remaining transparent to users?
The data archiving process typically uses automated software that moves “cold” data according to policies set by an administrator. A popular modern approach is to make the archive transparent, so archived data remains online and fully accessible exactly as before by users and applications. In this model, users experience no change in behavior even though the data has been archived or tiered. The patented Komprise Transparent Move Technology is designed to transparently archive and tier data while preserving user and application access.
Archival storage provides a cost-effective way to manage data that is not needed for daily operations but must be retained for occasional access or regulatory requirements. Through data tiering, disk, object, tape, or cloud options, organizations can reduce primary storage costs while protecting older information. Automated and transparent archiving approaches further ensure that data remains accessible without disrupting users or applications.
How does archival storage fit into unstructured data management?
Archival storage is a key part of unstructured data management because it helps organizations move inactive or rarely accessed files off expensive primary storage while retaining access for compliance, governance, and future business needs. It supports better lifecycle management as file data continues to grow across NAS, cloud, and object storage.
What is the difference between archival storage and intelligent tiering?
Archival storage focuses on long-term retention of inactive data on lower-cost storage. Intelligent tiering goes further by analyzing file activity and automatically moving cold data to the right storage tier while preserving transparent user access. Tiering helps reduce costs continuously, while archival storage is one destination within a broader lifecycle strategy.
Why is archival storage more important now?
Organizations are facing rapid unstructured data growth, rising flash and primary storage costs, tighter budgets, and increased compliance requirements. Archival storage helps control costs by keeping inactive data on lower-cost tiers while reserving premium storage for active workloads and AI initiatives.
Learn more about Flash Stretch.
How does Komprise help with archival storage?
Komprise helps organizations identify cold data, transparently move files to archival storage, and preserve user access through intelligent tiering. This reduces storage costs, delays hardware upgrades, improves visibility, and supports governance across heterogeneous environments.
Can archival storage support AI and analytics initiatives?
Yes. By moving inactive files off premium storage, archival storage frees capacity for active AI workloads. Combined with unstructured data management, organizations can also identify valuable archived content that may be useful for AI training, retrieval, or analytics.
