Case Study: Walmart achieves near real-time retail analytics with Elastic

Walmart - Customer Case Study

Walmart Technology built a near‑real‑time retail analytics platform to process massive point‑of‑sale streams across 11,500 stores and hundreds of millions of customers. The challenge was ingesting and analyzing very high‑volume, low‑latency data (their Elasticsearch footprint included 18 nodes, 60 indices, ~1,800 shards, ~5 TB and roughly 6 billion documents) to detect price anomalies, monitor gift‑card sales, warranty trends, cash availability and other operational KPIs.

They implemented a streaming architecture—POS → Kafka → branched processing (Flume→Hadoop, Storm→Cassandra, Storm→Elasticsearch)—with bulk‑ingest tuning and Elasticsearch X‑Pack (Shield, Marvel, Watcher) for security, monitoring and alerting. The result was near‑real‑time dashboards and automated watches that surface price inconsistencies, sales anomalies (the “bananas” example), and store‑level operational issues, enabling faster business decisions and improved store monitoring.

Walmart

Kevin Conaway

Engineer