Lifecycle storage tiering for compliance logs

WHAT THIS TESTS This evaluates whether you can design an automated, tiered lifecycle that aligns storage cost with how the data is actually accessed over time.

A GOOD ANSWER COVERS The access pattern has two phases: hot for 7 days, then cold for the rest of the year, with a hard one-year retention requirement. Store new logs in a standard tier where queries and analytics run cheaply on reads. After 7 days, a lifecycle policy transitions objects to an infrequent-access tier such as S3 Standard-IA or Azure Cool, which lowers storage cost in exchange for retrieval fees. Around 30 days, transition again to a deep archive tier like S3 Glacier Deep Archive or Azure Archive for the bulk of the year, since the data is rarely touched. Finally an expiration rule deletes objects at 365 days to stop paying for data past its retention. Transitions are driven declaratively by object age, not by application code.

COMMON WRONG ANSWERS Keeping everything in standard storage, which is simple but expensive at petabyte scale. Putting the active 7-day window in archive, where retrieval takes hours and incurs large fees, breaking the query SLA. Forgetting the deletion rule, so storage cost grows forever. Ignoring minimum storage duration charges that penalize moving data to archive too quickly.

LIKELY FOLLOW-UPS How do you query archived data when an audit requires it. How do you handle a legal hold that must override deletion. Would Intelligent-Tiering simplify this.

ONE CONCRETE EXAMPLE Logs land in S3 Standard. A lifecycle rule moves them to Standard-IA at day 7, to Glacier Deep Archive at day 30, and expires them at day 365. Analysts query the recent week directly, and the rare audit restores archived objects on request.