IBM Message Hub (MH) now supports a bridge which continuously uploads a topic's messages as objects in IBM Cloud Object Storage (COS). IBM Cloud Object Storage is IBM’s latest generation, scalable cloud storage service, designed for high data durability, resiliency and security.
How an integrated data science platform can transform your ability to make productive use of big data Leverage an integrated data and analytics toolset that makes data science smooth and seamless Unlock self-service big data analytics for users of all skill levels – from data scientists to citizen analysts Get a new end-to-end data science workflow up and running within a day
IBM Cloudant, a fully managed JSON document store, is a key component in many applications built on the IBM Cloud. In an effort to reflect the consolidated offering and Cloudant's place in the IBM Cloud ecosystem, the @IBMcloudant Twitter handle will become inactive on January 8, 2017, as we bring our IBM Cloudant- focused messaging to the IBM Cloud social channels.
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.
We hope you have been having a great experience discovering, cataloguing and governing data with IBM Data Catalog as part of IBM Watson Data Platform. We’d like to inform you that the Data Catalog service is now generally available (GA), and all Beta plan instances will be retired on January 31, 2018.
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.
Many organizations have started to explore the value that machine learning can bring—from illuminating previously “dark data” such as images and videos, to creating models that help to guide or even automate business decision-making. However, very few companies have gone beyond pilots and prototypes, or made the transition from one-off projects to a scalable, repeatable workflow. Too often, machine learning exists in a bubble of its own, instead of being understood in the context of the broader data science workflow.
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.