A real-time analytics solution that turns fast-moving volumes and varieties into insights. Streams evaluates a broad range of streaming data — unstructured text, video, audio, geospatial and sensor — helping organizations spot opportunities and risks as they happen. Its Eclipse-based, visual IDE lets solution architects visually build applications or use familiar programming languages like Java™, Scala or Python. Data engineers can connect with virtually any data source — whether…
There are well explained tutorials to get the user started. If you are looking for business application ideas, the user community offers a diversity of applications. It is very easy to launch applications on the cloud and can integrate with other analytic tools available on …
We are using Spark streaming as well as Storm for streaming options. Currently streams provides a better way of building applications easier faster and run efficiently. Also like the flexibility it provides with both us and SPL.
I have considered Apache Spark Streaming and Apache Flink. Spark Streaming is still changing too often for my taste and does not seem as easy to connect to IoT data especially for students having limited experience with cloud computing. Interesting signal processing functions …
Basically, I am building an IoT project. IBM cloud is a great platform for connecting all kinds of functions and make it work. To me, IBM Streams is just one of them. Any IoT project is custom made. So engineers have to think carefully how to use least resources to make the …
The selection of a stream processing platform depends heavily on the details of the requirements. There is no one right answer for all situations. However, IBM Streams typically has the advantage when sub-millisecond latency is important, complex analytics need to be …
Like the name says, it is good for streaming data and analyzing. It is great to look at tuples at a fast rate, filtering, calling other sources to enrich data, can call APIs, etc. Could do better for ingest use cases, can do better with guaranteed delivery, etc.
IBM Streams is well suited for providing wire-speed real-time end-to-end processing with sub-millisecond latency.
Streams is amazingly computationally efficient. In other words, you can typically do much more processing with a given amount of hardware than other technologies. In a recent linear-road benchmark Streams based application was able to provide greater capability than the Hadoop-based implementation using 10x less hardware. So even when latency isn't critical, using Streams might still make sense for reducing operational cost.
Streams comes out of the box with a large and comprehensive set of tested and optimized toolkits. Leveraging these toolkits not only reduces the development time and cost but also helps reduce project risk by eliminating the need for custom code which likely has not seen as much time in test or production.
In addition to the out of the box toolkits, there is an active developer community contributing additional specialized packages.
There are well explained tutorials to get the user started. If you are looking for business application ideas, the user community offers a diversity of applications. It is very easy to launch applications on the cloud and can integrate with other analytic tools available on Watson Studio. It takes away the burden of the technology so that users can focus on business innovations.