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With your own Aster cluster installed and running (Getting Started with Aster Express), and a few Aster nPath examples under your belt (On the Road to nPath), it's time to unleash the full analytic power of Aster and install the remaining SQL-MR libraries.  These Aster Analytic modules are a powerful suite of reusable SQL-MapReduce® functions that deliver advanced analytics on Big Data with immediate business impact by leveraging the power of MapReduce programming through standard SQL.

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! 

As most of you might agree, managing our collections of digitial pictures is becoming quite a challenge.  The number of photos continues to increase and now includes pictures from cameras as well as multiple mobile devices.  And to add to my troubles, I find that I have duplicate copies in different folders and on different computers.  Getting this organized is becoming a high priority.  Sure there are management solutions already available, but hey, we're tech people and it's more fun to try to build our own!  With the free Teradata Express database and some java coding, we have the right tools to get started.

Teradata has added geospatial features to Teradata 13 (and earlier versions with the optional extension package - see my earlier article here).  These features enable powerful location based analytics, but often I'm asked how to get started, especially by customers who already capture Latitude/Longitude location data.  So to help, I've put together this quick 2 minute guide on converting your existing location data to the new ST_Geometry data type in Teradata so that you ca

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