Teradata Aster is an analytic platform that embeds MapReduce analytic processing with data stores.

  • Embedded MapReduce analytic processing and unique SQL-MapReduce® framework
  • Massively parallel data store for multistructured data
  • Intuitive tools and SQL-MapReduce libraries for rapid analytic development

Also, be sure to read The Data Blog, where the visionaries at Teradata Aster share their on big data and advanced analytics. For community support, please visit the Aster forum.

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Introducing Aster Lens

Aster Lens is a new interactive Web application for Aster 5.10.  It allows Users to find, view, and share results from their nPathViz and cFilterViz functions.  It’s a quantum leap forward compared to the old way of visualizing where the answer set would provide you with a URL that you have to Copy/Paste into a Web browser.  This presentation will cover the basics on how to setup, configure and use Aster Lens.



Hands-on with Teradata Aster Express

The new Teradata Aster Express virtual images bring the powerful analytics of the Aster platform to any PC or workstation. 

Aster Database support in Teradata Studio Express 14.02

Teradata Studio Express 14.02 now supports Aster database connectivity. Teradata Studio Express is an information discovery tool for retrieving and displaying data from your Aster database systems. It can be run on multiple operating system platforms, such as Windows, Linux, and Mac OSX. It is built on top of the Eclipse Rich Client Platform (RCP) which allows Teradata Studio Express to benefit from the many high quality Eclipse features available while focusing on value-add for the Aster database.

Aster nPath functionality (Volume 2)

I’m assuming you’re already read Aster nPath functionality (Volume 1) and and are now ready to go into a deep dive with Use Case scenarios.  First we’ll cover some additional concepts, and then move into real-life examples that bring out the value of nPath.  Here’s the lesson plan:

Aster's Linear and Logistic Regression functions

Regression is a method of doing analysis.  Basically it helps you predict the future.  Businesses use these models to help explain customer behavior which can make them more profitable. 

But wait a minute, who started this whole Regression thing?  The guy who invented Regression, Sir Francis Galton, was studying how the height of fathers predicted the height of their sons.  He showed on average that short fathers had taller sons and tall fathers had shorter sons.  He called this condition ‘regression to mediocrity’ and the term stuck.

Aster's Market Basket and Collaborative Filtering functions

Retailers mine transaction data to track purchasing behavior.  Some of the more popular are Market Basket and Collaborative Filtering

By understanding what products customers tend to purchase, a vendor can maximize their sales for that customer.  Armed with this information, an analyst can initiate:

Aster Unleashed - Installing the Analytic Libraries

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.

Aster nPath functionality (Volume 1)

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

Configuring Replication Factor = 2 in Aster Data

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.

Using Aster Data's Naive Bayes functions

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.