Raptor combines Hadoop & HBase with machine learning models for adaptive data segmentation, partitioning, bucketing, and filtering to enable ad-hoc queries and real-time analytics. Raptor has intelligent optimization algorithms that switch query execution between HBase and MapReduce. Raptor can create per-block dynamic bloom filters for adaptive filtering. A policy manager allows optimized indexing and autosharding. This session will address how Raptor has been used in prototype systems in predictive trading, times-series analytics, smart customer care solutions, and a generalized analytics solution that can be hosted on the cloud.