Data storage is an ongoing process and the Walrus system is built around this fact. The very moment data enters the network, it embarks on a distinct data lifecycle that the system dynamically manages. Its approach to data lifecycle helps $WAL data remain consistent and reliable even after long data storage sessions.

At first, when data is stored, Walrus assigns data to various providers depending on network conditions. As time passes, these conditions change. Nodes go down, new providers appear, and levels of performance change. @Walrus 🦭/acc constantly determines whether data still satisfies "requirements of availability and integrity throughout its lifecycle".

With the passage of time, the network does not presume that the former assumptions are still valid. The tests for availability are carried out, and any drop in availability leads to corrective measures. New suppliers are allotted if necessary, while obsolete or malfunctioning replicas are upgraded with minimal user interaction. In this way, gradual degradation of storage availability is avoided.

The incentives for $WAL are integrated into this process. Providers collect rewards only while they have a responsibility for healthy and active replicas of data. The provider stops receiving rewards or incurs responsibilities when he or she leaves early or does not meet a requirement. The storage responsibility remains a guarantee for the entire lifetime of the data.

This has particular implications for users because data will be protected and preserved in an active rather than passive way. The network will dynamically adjust as circumstances evolve and maintain original availability objectives through storage and even long-term retention.

Walrus views data as an obligation, not a transaction finished once and for all. Through dynamic storage management along its lifecycle, and lining performance to $WAL rewards, it ensures its reliability despite its growth and evolution over time. #walrus