Graph database vs distributed SQL for identity resolution

Identity resolution systems in advertising typically require rapid ingestion of new device-to-household links, one- or two-hop lookups, concurrent frequency reads, and large analytical joins rather than the deep, dense-network traversals for which native graph databases are designed. Nightly global recomputation can be avoided by sharding identity data around stable keys such as households, allowing new signals to affect only a local partition, although cross-household edges may require more costly cross-shard processing. Distributed SQL engines are presented as a stronger fit because they can combine transactional writes with immediate analytical reads on the same fresh data, support high advertiser-dashboard concurrency, and store structured edges, JSON signals, and vector embeddings together for probabilistic matching. Native graph databases remain appropriate for workloads requiring five or six hops through complex networks, but platforms limited to short traversals and constrained by freshness, rebuild latency, and serving demand are better understood as partitioning and real-time SQL problems.

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