Cloud Streams

Streaming data and real-time analytics

Easily handle millions of events per second with in-stream ETL and analytics


It’s not enough anymore to simply perform historical analysis and batch reports. In situations where you need to make well-informed decisions in real-time, the data and insights must also be timely and immediately actionable. Cloud::Streams lets you process data as it flows into your application, powering real-time dashboards and on-the-fly analytics and delivering data seamlessly to Hadoop clusters and NoSQL databases. Single-purpose ETL solutions are rapidly being replaced with multi-node, multi-purpose data integration platforms — the universal glue that connects systems together and makes Big Data analytics feasible. Cloud::Streams is a linearly scalable, fault-tolerant distributed routing framework for data integration, collection, and streaming data processing. Ready-to-go integration connectors allow you to tap into virtually any internal or external data source that your application needs.


  • Easily integrate with virtually any data source, both live/in-motion as well as bulk/at rest
  • Process data as it flows, at scale – not only generating real-time insights, but also delivering data to databases and Hadoop clusters that has already been cleaned, transformed, and augmented/enhanced
  • Solve any business use case with the ability to handle any complexity business logic and parallel stream computing
  • Write your analytics once when leveraging Wukong – then run in both real-time with Cloud::Streams and in batch with Cloud::Hadoop


  • Process streams of data and update databases in real-time utilizing using parallel processing of live streaming data
  • Process millions of records per second of data collection, distributed ETL (extract, transform, load), or in-stream / in-memory analytics
  • Continuous queries with streaming real-time results. An example is streaming trending topics on Twitter into browsers. The browsers will have a real-time view on what the trending topics are as they happen
  • Parallel querying capability
  • Standard and seamless integration connectors that connect to any data source (internal or external)
  • Tool set for rapid development of processors to perform in the real-time stream processing (cleansing, normalization, and real-time analytics)