Capture and analyze data in motion

IBM Streams is an advanced analytic platform that allows user-developed applications to quickly ingest, analyze and correlate information as it arrives from thousands of data stream sources. The solution can handle very high data throughput rates, up to millions of events or messages per second.

Streams helps you:

Analyze data in motion

Simplify development of streaming applications

Extend the value of existing systems

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It's not just the fact that you can do this 100 times faster than before. It's that you can begin to treat your data with a fair degree of discrimination and refine data concerning multiple phases in a way that was not previously possible.

- Dr. N. Stewart McIntyre, professor emeritus of chemistry

What's new in IBM Streams

Announced October 20, 2015, IBM Streams v4.1 includes new features designed to address the important issues companies are facing. Open source technologies such as Spark are taken into consideration along with reducing time-to-value through use of the Java programming language. Corporate and governance concerns were also addressed through data lineage and data governance.

With IBM Streams V4.1, you can:

Details of the new capabilities in IBM Streams

Faster streaming application delivery

To speed things up, IBM Streams v4.1 has added the ability for developers to create Streams applications in Java. Since Java has widespread usage, the ability to use it to create applications helps reduce the learning curve for many developers and thus accelerate the rate at which applications can be produced. In fact, a developer with no prior knowledge of Streams can create Streams applications in under an hour using Java APIs for streaming analytic libraries such as natural language processing, spatial, temporal, acoustic, image recognition and more.

More intelligent applications

IBM Streams v4.1 increases application intelligence with the integration of open source technologies such as Spark and Hadoop through Java APIs. This means that data streams are captured efficiently and used alongside other data at rest (Hadoop, databases, and more). In addition, Spark and IBM Streams complement each other, with Spark working well for data at rest and IBM Streams excelling at event driven low latency apps. IBM Streams also has the broadest range of machine learning, adding Spark and MLlib to existing Streams Native Machine Learning, SPSS, R and PMML, making even more sophisticated analytics possible.

More confidence in data stream insights

Many executives are still relying on their gut, revealing a lack of confidence in insights. Yet, strong governance can counter these feelings of doubt and help meet corporate mandates. IBM Streams v4.1 introduces the creation of data lineage and use of flexible schemas for easier data ingestion. It also enables automatic schema discovery and mapping through integration with IBM InfoSphere Data Governance Catalog. The added assurance of data quality and reliability can be the difference between a gut check decision and one backed by defensible insight.

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