Put data where the reads are, without losing the record of truth.
Coordinate object storage, key-value, relational, and vector data across regions and providers.
Storage decisions outlive almost every other architectural choice. Object stores, key-value caches, relational databases, and vector indexes each answer a different question, and the cost of choosing wrongly compounds quietly through egress fees, replication lag, and queries that cannot be served where the request arrives.
This is where distributed systems get expensive. Data lands in one region while traffic arrives in another. Caches drift from the source of record. Transfer charges make the obvious architecture the wrong one.
Distributed Data & Storage gives that layer a coordinated shape. It maps which data is authoritative, which is derived, and where each belongs across R2, S3, Cloud Storage, Blob Storage, and the database and index tiers alongside them. What matters next is locality, consistency, and cost that behaves the way the team expects.
Start with the most expensive read —
Pick one query or asset path where distance, egress, or replication lag already produces cost and latency the team can name.
Separate authoritative from derived —
Establish which store holds the record of truth and which are caches, projections, or indexes that can be rebuilt.
Model the cost before the migration —
Price storage, requests, and egress across candidate providers so placement is decided on numbers rather than familiarity.
Outcomes
Data close to the request —
Reads are served from the right region and tier, with less round-trip distance and fewer cross-region dependencies.
Clearer consistency boundaries —
The team knows which store is authoritative, what may be stale, and how long staleness is acceptable.
Egress under control —
Transfer and request costs become visible and budgeted rather than discovered at the end of the quarter.