Data and persistence
Code comes back from a repository. State created during use does not.
How persistent data moves through writing, reading, growth, retention and disposal, and why its meaning guides the architecture.
Data lifecycle
Persistence involves more than storage
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Write Rules preserve meaning
Validation and transactions keep relationships coherent.
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Access Each application uses only what it needs
Access follows the role and data set of the project.
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Evolve Volume and structure change with the product
Growth requires reviewing queries, indexes and retention.
Persistence preserves state and meaning
Records represent orders, registrations, permissions and domain relationships. Integrity depends on rules that prevent a partial update from leaving the collection in a contradictory state.
Transactions treat related operations as one unit. The project defines what can be repeated, what requires confirmation and what cannot fail silently.
Access follows application responsibility
The data layer does not need to be open to the public network. The application receives the access required for its own information while communication stays within the intended path.
Parameterized queries separate instructions from values. This code-level control complements the network boundary and the permissions used for persistence.
Growth requires choices about structure and retention
Volume changes read and write costs before disk space runs out: queries without indexes, long histories and heavy reports show up first in response time.
Retention is also a product and privacy decision. Keeping information without a purpose increases volume and responsibility; removal requires clear rules and copies consistent with that lifecycle.