DataTorrent Fortifies Its Streaming-Data Management Toolset

Updated version of data management platform brings real-time historical trend analysis, expanded support for machine learning and other capabilities for building and maintaining fast data applications.


DataTorrent, one of the few data management companies that provides big data analytics by using real-time, data-in-motion stream processing, has announced a significant update to its platform.

The company said Feb. 22 that its real-time streaming platform, DataTorrent RTS 3.10, which is for building, deploying and operating real-time streaming data applications, now contains added functionality to develop fast data applications.

Managing data in motion is different from managing data at rest. Event stream processing relies on three principal capabilities–aggregation, correlation and temporal analytics–to deal with data in motion. Event stream processing continuously analyzes data as it flows into the organization, then triggers an action based on the information flow.

It is a form of complex event processing that empowers users (or an automated system) to spot patterns and make decisions faster and more efficient.

Apoxi Framework Provides 'Glue' to Bind Applications

The platform is supported by DataTorrent’s RTS Apoxi framework, which provides the tools required to assemble, manage and operate fast data applications on any infrastructure. Apoxi is a framework that binds components together to create optimized, pre-built applications and can also integrate independent applications to allow them to operate as one.

“Companies everywhere are keenly aware of the need to gain insight from data to become more customer-centric, improve operational performance and create new revenue streams,” CEO Guy Churchward said. “Our updates are not only designed to help organizations make better decisions faster, but they are part of our strategy to fundamentally change the way big data applications are designed, deployed and managed."

DataTorrent’s cloud service is designed to be a continuous-learning application, Churchward said.

“We sit down with the customers, we’ll counsel them, and we’ll train their staff up. It’s sort of like teaching a kid to get rid of the training wheels, and then when they’re kicked off, they can cycle themselves,” Churchward told eWEEK. “We’re not a company that jams an intravenous dollar bill tube into their arm and say, ‘You can just keep paying us.’

SaaS Model Offers Options

“We’re about IP, and what we do is basically charge a subscription model for the use of our products on an annual basis, and that’s it. There’s no fixed term for it.”

New features in RTS 3.10 include:

  • Support for OLAP (online analytical processing) with Druid (an open-source data store designed for sub-second queries on real-time and historical data) provides customers with the ability to slice and dice data in real time to get the information needed to compute and compare it to historical trends. This capability is delivered as a pre-built component that integrates Apex, Druid and SuperSet for real-time BI dashboards and visualizations.
  • Expanded support for machine learning and AI helps customers capitalize on the value from the latest innovations in data science. This includes native support for the delivery of analytical logic using machine scoring models written in Python or Predictive Model Markup Language (PMML).
  • Store and replay helps customers push to production with confidence. Customers can record and replay data from a point in time to evaluate the effectiveness of builds, models, and scenarios before they are put into production, removing the guesswork about what outcomes will occur.
  • Drools Workbench integration makes it easier to modify complex event processing (CEP) rules and push new rules to production. The workbench enables customers to import data schema and visually edit and manage complex rules easily by using an intuitive graphical user interface.
  • Application backplane enables multiple applications to be simply and consistently integrated in order to share insights and actions. Combining numerous applications in real-time can result in significantly better outcomes, while still enabling separately managed applications to remain independent and benefit from a network-effect.

DataTorrent also is introducing a set of new applications focused on the financial services and retail verticals. AppFactory is DataTorrent’s marketplace for big data streaming analytics use cases, reference architectures, and downloadable applications arranged by industry or technology. The new additions include:

  • Omni-channel payment fraud prevention: The newest version of DataTorrent’s Omni-channel Payment Fraud Prevention application integrates with the Druid OLAP component for real-time online analytical processing and enhanced historical trend analysis. This latest application also includes a reference architecture for integration with a variety of machine-trained analytical models for enhanced fraud prevention.
  • Online account takeover prevention: A reference application that enables customers to prevent online account takeover and fraud by processing, enriching, analyzing, and acting on multiple streams of account event information in real-time.
  • Retail recommender: Real-time, personalized product and service recommendations drive additional revenue for retail and ecommerce companies. DataTorrent’s Retail Recommender provides a reference architecture that produces product recommendations in real-time by operationalizing the latest innovations in machine-learning.

For more details about RTS 3.10, go here.

Chris Preimesberger

Chris J. Preimesberger

Chris J. Preimesberger is Editor-in-Chief of eWEEK and responsible for all the publication's coverage. In his 15 years and more than 4,000 articles at eWEEK, he has distinguished himself in reporting...