Message Hub provides a simple communication mechanism built on Apache Kafka, enabling communication between loosely coupled Bluemix services. This article shows how to communicate with Message Hub from the Streaming Analytics Bluemix service using the messaging toolkit.
The Streaming Analytics service in the IBM Cloud is an advanced analytic platform allowing user-developed applications to quickly ingest, analyze, and correlate information as it arrives from a wide variety of real-time data sources. Today, an enhanced version of the service has been released as a beta, to introduce some exciting new features.
From dreams to streams: turning the vision of streaming analytics into practical business reality with IBM Streams Designer
Today’s web is a much more open place than ever before—most social networks and other web platforms offer public APIs that allow anyone to request and use data on a scale that would have been unthinkable just a few years ago.
There’s a lot of hype around the possibilities of stream computing. It seems like everywhere you look, more and more organizations are touting the benefits of capturing and analyzing large volumes of data at high velocity—and increasing numbers of streaming analytics solutions, both commercial and open source, are flooding the market.
Change doesn’t stop, so neither should your analytics. You could capture the most crucial, valuable insight of all—but if you don’t identify and act on it while it’s still valid, or before your competitors do, it’s worth nothing. Imagine you’re an electronics company that has sunk thousands of hours and millions of dollars into building a profile of the perfect customer for a new product release. Before you can claw back your investment with a wildly successful launch, a rival comes along and disrupts the entire industry with an innovative device like no one has ever seen before. All that effort and resources expended… all for nothing.
The world doesn’t stop, which also means that data never stops pouring in. If you’re in the analytics game, then basing your efforts on a snapshot of historical data always involves a degree of compromise. Did you choose the right data set; one that is an accurate representation of ongoing operations so that it doesn’t skew your analysis? How soon will your insights be out of date? How can you store the data that you’re analyzing cost-effectively?