Large Retailer Modernizes Customer Identity Resolution with a Near-Real-Time Graph
The Challenge
The large retailer needed to modernize batch-driven identity resolution into a near-real-time OLTP identity graph, linking accounts, phones, emails, addresses, tokens, devices, and transaction/profile signals as events arrive.
The Solution
TigerGraph helped the large retailer design a graph-native identity model, validate customer boundary traversal, optimize ingestion architecture, tune serving queries, and prepare monitoring, backup, HA, and disaster recovery requirements.
The Results
TigerGraph helped the large retailer design a graph-native identity model, validate customer boundary traversal, optimize ingestion architecture, tune serving queries, and prepare monitoring, backup, HA, and disaster recovery requirements.
The Challenge
The large retailer is modernizing its Customer Identity and Entity Resolution platform from a batch-oriented process into a near-real-time OLTP identity graph. Today, identity resolution is largely batch-driven, with entity matching and customer boundary creation running in Spark with meaningful delay.
The business need is to resolve customer identities faster by linking accounts, phones, emails, addresses, tokens, devices, and transaction/profile signals as events arrive, so downstream systems can act on the freshest possible customer identity view.
The business need is to resolve customer identities faster by linking accounts, phones, emails, addresses, tokens, devices, and transaction/profile signals as events arrive, so downstream systems can act on the freshest possible customer identity view.
The Solution
The large retailer needed to support a real-time customer identity graph that could ingest, resolve, traverse, and serve customer relationships at OLTP speed. The challenge was not just ingestion volume; it also required reliable serving performance and production readiness at scale.
The team needed high-throughput profile and transaction signal ingestion, customer boundary traversal after data lands, low-latency serving, and confidence that the platform could handle production-scale data estimated in the multi-terabyte range.
The large retailer also needed production-grade observability, backup, disaster recovery, monitoring, HA planning, RTO/RPO considerations, and upgrade readiness. The identity graph had to operate as a production OLTP service, not just a POC.
The team needed high-throughput profile and transaction signal ingestion, customer boundary traversal after data lands, low-latency serving, and confidence that the platform could handle production-scale data estimated in the multi-terabyte range.
The large retailer also needed production-grade observability, backup, disaster recovery, monitoring, HA planning, RTO/RPO considerations, and upgrade readiness. The identity graph had to operate as a production OLTP service, not just a POC.
The Results
TigerGraph helped the large retailer turn a batch-based identity resolution pattern into a graph-powered OLTP architecture capable of near-real-time identity linking, customer boundary discovery, and scalable serving.
The solution gives the large retailer a path to reducing identity resolution lag, improving freshness of customer intelligence, and supporting high-volume transaction/profile updates. Large Retailer was able to test upsert throughput in the 6K–7K QPS range while continuing to evaluate Kafka-based ingestion for production readiness.
The architecture also gives the large retailer a production-grade identity graph foundation for future fraud, risk, personalization, and customer intelligence use cases, with monitoring, loading visibility, log integration, backup, restore, disaster recovery, HA, and upgrade planning requirements addressed.
The solution gives the large retailer a path to reducing identity resolution lag, improving freshness of customer intelligence, and supporting high-volume transaction/profile updates. Large Retailer was able to test upsert throughput in the 6K–7K QPS range while continuing to evaluate Kafka-based ingestion for production readiness.
The architecture also gives the large retailer a production-grade identity graph foundation for future fraud, risk, personalization, and customer intelligence use cases, with monitoring, loading visibility, log integration, backup, restore, disaster recovery, HA, and upgrade planning requirements addressed.
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