Friday, November 8, 2013

Big Data Scoring with UPPI for IBM Pure Data (for Analytics and Hadoop)

In-database scoring is one of the most straightforward ways to gain insights from Big Data. It is no surprise then that the Zementis Universal PMML Plug-in (UPPI) is now being offered for a variety of database platforms. These include IBM Pure Data for Analytics (Netezza), Pivotal/Greenplum, SAP Sybase IQ, Teradata and Teradata Aster. Zementis also offers UPPI for Hadoop/Hive, including IBM Pure Data for Hadoop as well as InfoSphere BigInsights. It is in this context that we travelled to Vegas to attend the IBM Information on Demand (IOD) Conference.

I must say, I am always impressed by the IBM universe of products and tools that are being offered for analytics (descriptive and predictive) as well as Big Data in general. Zementis had a booth inside the Pure Data exhibit area and next to all the Pure Data appliances. As you can imagine, traffic was solid not just because of all the blinking lights but also because the conference itself attracts a lot of people. I believe there were 14 thousand attendants this year.

Why in-database scoring? Well, simple. Not all analytic tasks are born the same. If one is confronted with massive volumes of data that need to be scored on a regular basis, in-database scoring sounds like the logical thing to do. In all likelihood, the data in this case is already stored in a database and, with in-database scoring, there is no data movement. Data and models reside together hence scores and predictions flow on an accelerated pace.

Why scoring in Hadoop? 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. 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.

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.

Saturday, November 2, 2013

1-Click Launch for Big Data Scoring: ADAPA on AWS Marketplace

Clients benefit from our solutions by being able to use PMML, the Predictive Model Markup Language, to move their predictive models from IBM SPSS, R, SAS EM, ... and deploy them instantly in a variety of platforms, including the Amazon Elastic Compute Cloud (Amazon EC2).

ADAPA on the Amazon Cloud offers the power of our real-time PMML-based scoring engine on the Amazon Cloud. ADAPA on the Amazon Cloud comes pre-installed on a virtual server on the cloud. We call that an "ADAPA Instance".

The AWS (Amazon Web Services) Marketplace gives you the power of having ADAPA at your fingertips on three different types of virtual machines. Once you select the machine type and the cloud region in which you want it to run (US, Europe, Latin America or Asia-Pacific), all you need to select is 1-Click Launch and moments later your ADAPA instance is up and running, ready for deployment and execution.

Visit us at the AWS Marketplace!

Big Data Scoring through ADAPA with S3 Processing

Zementis makes it super easy to score your big data by connecting your Amazon S3 (Simple Storage Service) bucket to your predictive models deployed in ADAPA on the Amazon Cloud. ADAPA with S3 Processing is intended for mission critical applications that require very high throughput of predictive analytics. While ADAPA provides real-time scoring via a Web-services API, S3 Processing addresses use cases with scoring requirements that involve tens or hundreds of millions of rows at a time.

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