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Earlier this year I posited that, due to the exponential rate of growth, the amount of data collected for analysis is becoming beyond the scope of the current analytical staffs to examine it all.  And that the answer to growing the cumulative brain power necessary for this exponential growth in analysis is going to have to be machine learning and artifici

Background

This article is a follow on to article [1] which discussed implementing K-means using a Teradata release 14.10 table operator. The main contribution of this article is to discuss how to use the new Teradata 15.0 multiple input stream feature and a short discussion on a gcc compiler performance optimization.

This article describes how to use Teradata query grid to execute a Mahout machine learning algorithm on a Hadoop cluster based on data sourced from the Teradata Integrated Data Warehouse.

Teradata ADS Generator, part of the Teradata Warehouse Miner family of products, was built to support both comprehensive data profiling as well as analytic data generation for Teradata customers.  Neither the data profiling nor the analytic data set generation capabilities of the product require any movement of data outside of the warehouse and utilize as