This presentation will cover some of the finer points on Aster’s nPath function.  Here’s the overview:

Since taking the Aster Data class, I wanted to learn more about this exciting new technology.  After downloading the 2 VMware images (Queen and Worker), I needed another Worker node to be able to increase the Replication Factor (RF) from 1 to 2.

Naïve Bayes is a set of functions to train a classification model.  A training data set for which we know the outcome (Predictor column) based on input variable columns are used to generate the model. 

We then run the model against a set of input variables for which we do not know the Predictor to see what the model says.  It’s quite similar to a Decision Tree with one big exception; the input data are independent of one other.  This is a strong assumption but it makes the computation of the model extremely simple.

One of the keys to Aster’s parallel processing power are Virtual Workers (or v-workers).  V-Workers are the compute and storage units in an Aster cluster, much like AMPS are for Teradata.  In this article, I’ll show you how to easily add another v-worker in a procedure called ‘partition splitting’.  This will double the “parallel computing” of our Aster Express cluster.

As we've seen in our introduction to the power of Aster's nPath (Using Aster Express: Act 3, On the Road to nPath), the nPath function allows you to perform regular pattern matching over a sequence of rows. With it, you can find sequences of rows that match a pattern you’ve specified and easily extract information from these matched PATTERNs using SYMBOLs that represent the matched rows in the pattern.

nPath uses regular expressions because they are simple, widely understood, and flexible enough to express most search criteria. While most uses of regular expressions focus on matching patterns in strings of text; nPath enables matching patterns in sequences of rows.

The foundations of the Aster platform are a parallel database with all the features you'd expect from a SQL platform.  However the power of the Aster platform really shines once you start using its library of SQL-MR analytic components.  With these components, complex data analysis that is difficult to code in SQL becomes much, much easier.  A clear example of this can be seen with Aster's nPath module.

In this second part of our series on using Aster Express, we'll continuing using the Aster ACT query tool and introduce a new tool for bulk loading data, Aster's ncluster_loader.  (See Using Aster Express: Act 1 for part 1 of this series).

Once you have an Aster cluster running (see Getting Started with Aster Express), it's time to start doing some work.  We'll start with the Aster database basics in this first part of a 3 part overview.

We are very excited to introduce the Aster Express for VMware Player images.  Much like the very popular Teradata Express program, these downloadable Aster virtual images will provide customers with a free evaluation version of the Aster analytic platform that can be run on their PC.  While this Express edition is not licensed for production usage, it is a fully functional Aster cluster that is a great tool for developers and testers or anyone else who wants a hands-on introduction to this Big Data analytics platform.  Over the coming weeks and months here on Teradata's Developer Exchange, we will be publishing Aster tutorials, along with sample datasets, that will highlight the powers of this exciting platform.

Aster Express virtual images are now available for downloading to your PC so that you can run an Aster cluster.  After installing VMware Player and downloading the Aster Queen and Worker images (see the Introduction to Aster Express article), you're ready to bring the Aster cluster to life!