Overview
Big Data and Hadoop are somewhat synonymous terms these days, since the latter offers an important technological platform to tackle the challenge of analyzing large volumes of data. In fact, predictive analytics is paramount for companies to extract value and insight from such data. It is in this context that Zementis brings its standards-based predictive scoring engine into a variety of Big Data platforms, including the cloud as well as in-database. By offering the Universal PMML Plug-in (UPPI) for Hadoop, Zementis takes a big step in making its technology available for companies around the globe to easily deploy, execute, and integrate scalable standards-based predictive analytics on a massive parallel scale through the use of Hive, a data warehouse system for Hadoop, and Datameer, an end-to-end BI solution that works on top of Hadoop.
UPPI brings together essential technologies, offering the best combination of open standards and scalability for the application of predictive analytics. It fully supports the Predictive Model Markup Language (PMML), the de facto standard for data mining applications, which enables the integration of predictive models from IBM/SPSS, SAS, R, and many more.
UPPI for Hadoop/Hive
Hive makes it possible for large datasets stored in Hadoop compatible systems to be easily analyzed. Since it provides a mechanism to project structure onto the data, Hive allows for queries to be made using a SQL-like language called HiveQL.
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Once deployed in UPPI, predictive models turn into UDFs (User-defined Functions). These can then be invoked directly in HiveQL. In this way, UPPI offers Hadoop users the best combination of open standards and scalability for the application of predictive analytics. UPPI for Hadoop/Hive delivers instant and scalable scoring for Big Data while retaining compatibility with most major data mining tools through the PMML Standard. It also brings brings the scalability of Hadoop to the execution of predictive analytics. |
For more details about the UPPI for Hadoop/Hive, feel free to: 1) contact us; 2) download the UPPI for Hadoop/Hive Product Data Sheet.
UPPI for Datameer
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Zementis and Datameer have partnered to deliver standards-based execution of predictive analytics on a massive parallel scale. This joint solution combines the Zementis plug-in for execution of predictive models with the power and scale of Datameer, an end-to-end BI solution that includes data source integration, an analytics engine, visualization and dashboarding. |
| Datameer uses Apache Hadoop, a Java-based framework that supports the parallel storage and processing of large data sets in a distributed environment, as its back-end storage and processing engine to scale cost-effectively to 4000 servers and petabytes of data. It provides wizard-based data integration to integrate large datasets of structured and unstructured data, integrated analytics with familiar spreadsheet-like interface and over 200 built-in analytic functions and drag and drop reporting and dashboarding visualization for end-users. Open API's for data integration, analytics and dashboarding make it easy to access custom data sources, utilize advanced or custom analytics like predictive modeling as well as custom visualizations. |  |
Predictive Scoring for Hadoop - Advantages
UPPI for Datameer delivers instant and scalable scoring for Big Data while retaining compatibility with most major data mining tools through the PMML Standard. Through its versatile deployment solution, the Zementis/Datameer partnership:
- Brings the scalability of Hadoop to the execution of predictive analytics
- Supports PMML to avoid time-consuming and expensive one-off predictive analytics projects
- Integrates data from multiple data sources and formats without complex data and schema mappings that are time consuming to set up and difficult to change
- Provides cost effective storage and processing of large volumes of highly granular data that predictive applications often require
- Brings together a 100% standards-based approach to analytics that lowers total cost of ownership and increases reuse control and flexibility for orchestrating critical day-to-day business decisions.
For more details about the Universal PMML Plug-in for Datameer, feel free to:
- Contact us
- Watch Part 1 of the Zementis/Datameer webinar series on "Predictive Analytics on Hadoop" featuring a presentation by Dr. Alex Guazzelli, our VP of Analytics, who starts with an introduction to predictive analytics and PMML, followed by a demo of the plug-in and Datameer
- Watch Part 2 of the Zementis/Datameer webinar series on "Predictive Analytics on Hadoop" featuring a presentation by Dr. Michael Zeller, our CEO, who starts with an introduction to PMML, Hadoop and model deployment, followed by a demo of the plug-in and Datameer (also featuring model building in KNIME)
- Watch a Video Tutorial which highlights how simple it is to leverage PMML-based predictive models in Datameer
- Watch the YouTube video of the Zementis/Datameer presentation at the 2012 Hadoop Summit entitled "Agile Deployment of Predictive Analytics on Hadoop"
- Download the Product Data Sheet
