Overview

This article provides an example of implementing a java table operator. The specific use case is support for the SHA-2 family of hash encodings, for more details on SHA-2 see https://en.wikipedia.org/wiki/SHA-2

Background on why a JAVA table operator was chosen as the implementation mechanism for SHA-2.

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.

XML is a markup language, used to format data in a wide variety of applications. It is commonly used as a message format for application integration (e.g. XML messages exchanged between applications, with those applications implementing an XML based API). Somewhat less commonly, it is used as a document format, to tag information in a platform independent manner. Starting in Teradata Database version 14.10, XML is supported as a native SQL data type.

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. Specifically the Mahout K-means cluster analysis algorithm is demonstrated.  K-means is a computationally expensive algorithm that under certain conditions is advantageous to execute on the Hadoop cluster. Query Grid is an enabling technology for the Teradata Unified Data Architecture (UDA).

Querying XML

Stored Procedures

SQL Stored Procedures were added to Teradata around 2003 with the release of Teradata V2R5.1. Since then the capabilities of SQL Stored Procedure s has been expanded. However, there are still some queries that cannot be run directly from within a Stored Procedure.

In a prior article [1] we described how to use the Teradata 14.10 CalcMatrix operator and R to perform a multiple variable linear regression analysis. This article extends that concept with a comprehensive in database solution by introducing a new in database table operator named “CM_Solve”. This approach has value in cases when you want to solve a large number of independent systems of equations or you simply do NOT want to use the R client for solving the system of equations based on the SSCP matrix.

Linear Regression

In statistics, linear regression is an approach to model the relationship between a scalar dependent variable y and one or more independent variables denoted x. Linear regression is one of the oldest and most fundamental types of analysis in statistics. The British scientist Sir Francis Galton originally developed it in the latter part of the 19th century. The term "regression" derives from the nature of his original study in which he found that the children of both tall and short parents tend to "revert" or "regress" toward average heights.

Table Operators

This article discusses how to implement a Teradata 14.10 table operator using K-means clustering as an example use case. 

As part of Teradata Database 14.10 Teradata Intelligent Memory (TIM) feature, a table function was developed to provide the heatmap report for a specific AMP for database object(s) for systems in which data temperature collection is enabled[1].     This HeatMap table function is being provided for sites that are interested in looking at the relative temperature of tables, which tables (cylinders) are targeted for the TIM cache and which tables are targeted for what temperature range