RapidMiner Streams Integrates With Apache Storm To Tackle Advanced Analytics On Streaming Big Data

RapidMiner today announces the release of RapidMiner Streams, an application that leverages the power of Apache Storm to enable analytics on real-time streaming data. By integrating RapidMiner’s predictive analytics technology with Apache Storm, RapidMiner Streams enables real-time updates to RapidMiner’s advanced analytics in collaboration with incoming, streaming data that dictate iterative modifications to existing algorithms. The release of RapidMiner Streams means that the RapidMiner platform can now perform advanced analytics on streaming data with low latency, high throughput and enhanced computational performance. The integration of Apache Storm with RapidMiner adds to the over 1500 RapidMiner operators and subsequently enhances the platform’s ability to perform real-time predictive analytics. Use cases for RapidMiner’s analytics include analysis of free text customer feedback delivered online, data mining of millions of image files and analytics on wearable devices that prescribe users with an optimal exercise regimen to achieve their fitness goals based on historical data. RapidMiner’s analytics are enabled by way of a graphical user interface that allows analysts to combine operators for the ingestion, analysis and visualization of one or more datasets. Today’s announcement of the release of RapidMiner Streams and its concomitant integration with Apache Storm vaults the RapidMiner platform to tackle data from the internet of things and embrace use cases featuring massive volumes of streaming real-time data. As the universe of use cases that RapidMiner can handle proliferates, users should expect corresponding enhancements of the platform as customers enrich the product with feedback specific to data about their product and vertical.


RapidMiner Cloud Delivers Cloud-Based Predictive Analytics Without Necessity To Write Code

RapidMiner today announces a cloud-based version of its predictive analytics platform that brings the benefits of cloud computing to its renowned machine learning and analytics technology. The generally available RapidMiner Cloud provides customers with an elastic computing platform, the capability to combine disparate data sources and a cloud repository that allows analysts to connect to the RapidMiner cloud and run analytics from a web-enabled machine. RapidMiner Cloud users can take advantage of the company’s preconfigured library of best practices for running analytics via a graphical user interface that absolves analysts of the need to write code as illustrated by the screenshot below:

Analysts can select different datasets and perform analytic operations without writing code by creating analytic workflows as shown in the graphic above. In addition to predictive analytics, RapidMiner Cloud provides support for data integration, data cleansing, data deduplication and data enrichment. As told to Cloud Computing Today in a phone interview with Ingo Mierswa, CEO of RapidMiner, the platform’s predictive analytic technology facilitates data enrichment by allowing analysts to use predictive algorithms to estimate values for missing or corrupt data. Moreover, RapidMiner Cloud integrates with over 300 cloud platforms including Amazon, Salesforce.com, Twitter and Dropbox. RapidMiner’s integration with Amazon S3, for example, means that data stored within S3 can be accessed by RapidMiner via a connector, and analyzed without the raw data being imported directly into the RapidMiner cloud platform. The platform’s ability to integrate with other vendors underscores the increasing importance of APIs and connector infrastructure components in a world in which organizations increasingly confront the challenge of data sharing across different platforms given the ubiquity of cloud-based data.