OLAP Cube is about pre-aggregations, it reduces the query latency by spending more time and resources on data preparation. But for real-time analytics, data preparation and visibility latency are critical. What happens when OLAP cube meets real-time use cases? Can we pre-build the cubes in real-time with a quick and more cost effective way? This is hard but still doable. In eBay,we built our own real-time OLAP solution based on Apache Kylin & Apache Kafka. We read unbounded events from Kafka cluster then divide the streaming data into 3 stages, In-Memory Stage (Continuously In-Memory Aggregations) , On Disk Stage (Flush to disk, columnar based storage and indexes) and Full Cubing Stage (with MR or Spark, save to HBase). Data are aggregated to different layers in different stage, but all query able. Data will be transformed from 1 stage to another stage automatically and transparent to user. This solution is built to support quite a few realtime analytics use cases in eBay, we will share some use cases like site speed monitoring and eBay site deal performance in this session as well. Speaker: Qiaoneng Qian, Senior Product Manager, eBay