You are designing the storage architecture for a financial analytics platform. The platform ingests and stores terabytes of transactional data daily, which is used for both real-time fraud detection a

Storing all data in Standard storage class satisfies latency requirements but is prohibitively expensive for petabytes retained over five years when most data is rarely accessed.

BigQuery time-partitioned tables with automatic long-term pricing reduce cost for old data but BigQuery is an analytics warehouse optimized for SQL queries, not the sub-millisecond key lookups required by a real-time fraud detection engine.

Cloud Storage Object Lifecycle Management can automatically transition objects from Standard (low-latency, high-cost) to Nearline after 30 days for infrequently accessed data, then to Coldline or Archive for long-term compliance retention at minimal cost. This matches all requirements: sub-millisecond access for recent data, acceptable seconds-range retrieval for historical data, and five-year retention at the lowest possible price point.

This option uses lifecycle management but transitions data to an incorrect storage class or uses incorrect age thresholds that do not align with the 30-day hot window and multi-year retention requirements.