The RichRelevance DataMesh Cloud Platform delivers a single view of your customer by:Giving you one single place to house unlimited sets of dataExample use cases:Create your own run-time strategies (predictive models)Create and manage segments via toolAutomatic & real-time segment creationView performance of strategies against KPIs Run adhoc queries using SQL-like toolImport into offline toolsOLAP capabilitiesMarket Basket AnalysisCustomer Lifetime ValueSequential Pattern miningManage APIs, build products & applications
Nuggets or Data Points1.5PB not as big as yahoo or facebook – huge from a retail industry perspective
Distributed System:: i.e. producers, brokers and consumer entities can all be deployed to different hosts in different colos in a truly distributed fashion and coordination controlled through zookeeperPersistence of Messages: messages need to be persisted on the broker for reliability, replay and temporary storagePush & Pull Mechanism:: i.e. push data to Kafka server and pull data from it using a consumer. This allows for two different rates: rate at which messages are transferred to the kafka server and the rate at which the messages are consumed.: Kafka supports GZIP and version 0.8 will additionally support Snappy compression.
HBaseCon 2013: Realtime User Segmentation using Apache HBase -- Architectural Case Study