WibiData Launches WibiEnterprise 3.0

By WibiData Staff,

TwitterFacebookLinkedInGoogle+RedditShare

WibiData Launches WibiEnterprise 3.0 to Power Real-Time Big Data Consumer Applications

Big Data Application Platform Drives Deep Customer Insight and Real-Time Application Evolution To Meet Individual Customer Needs at Scale

SAN FRANCISCO, November 25, 2013—Today WibiData, a pioneer in building Big Data Applications, announced the launch of WibiEnterprise 3.0, an enterprise software platform for developing and deploying real-time Big Data Applications. WibiEnterprise 3.0 bridges the gap between Hadoop and the application layer, empowering companies to connect consumer applications with sophisticated real-time, data-driven features like personalized content, predictive recommendations, dynamic micro-segmentation, personally relevant search results and anomaly detection. 

WibiEnterprise 3.0 is based on WibiData’s Kiji Project, an open source framework for building Big Data Applications, and is used in production by some of the world’s largest SaaS, retail and financial services companies. Kiji was conceived to directly address a common and growing problem: companies have invested heavily in capturing and storing their customer data, typically using a combination of products and services in the Hadoop and HBase ecosystems, but are falling short in utilizing their data and putting insight to use in the form of real-time, revenue-generating, dynamic applications. As a result, many companies are not yet seeing any return on their big data infrastructure investments. 

“Trying to solve big data challenges by combining distributed storage strategies with traditional application development processes mires even the most talented developers and data scientists in tedious and laborious modeling and development cycles,” Rick McPhee, Opower's SVP of Engineering said. “After facing some of these problems first hand, we’ve worked with WibiData to simplify and speed our big data application development processes.”

Sophisticated data modeling and in-application analytics often requires coordinating a complex integration of advanced data science, developer tools and corresponding talent.  WibiEnterprise 3.0 simplifies this process by allowing data scientists to explore data, develop and train models, and deploy the best models to production where they are scored on the fly, delivering real-time, individualized and contextually relevant experiences across application channels.  Application developers can use WibiEnterprise 3.0 to easily record information in real-time for use in dynamically updated predictive models, and deliver results to front-end applications, whether on the web, mobile devices, or other digital channels. WibiEnterprise 3.0 gives companies the ability to collaborate across functions, experiment with the best analytical models and create better application experiences by reducing friction in sales and servicing across application channels.

“Despite the hype around big data, companies have lacked the tools and technologies to efficiently deliver data-driven features like individualized, contextual customer interactions and experiences across devices at multi-million user scale,” Christophe Bisciglia, Founder and CEO of WibiData, said.  “WibiEnterprise 3.0 is the next step in filling this void and will ultimately serve as the platform from which we launch vertically integrated, industry specific solutions that help retailers, financial services companies and other consumer focused enterprises better serve their individual customers and dynamically adapt the application experience to meet their customers’ needs and business goals.”

The core features of WibiEnterprise 3.0 are:

  • Schema Management: a framework for defining tables and datasets that allows for consistent evolution over time without downtime.
  • Batch Processing: a framework for bulk imports, complex analysis tasks and other MapReduce jobs
  • Machine Learning Model Lifecycle: a framework for authoring machine learning models, batch model training, model deployment and real-time model scoring.
  • Data Exploration: a framework for ad hoc query and analysis of application customer data.
  • Application Integration: a framework for integrating with other applications through RESTful interfaces for reads, writes and predictive model scoring.

Uncategorized