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RevoScaleR Teradata Getting Started Guide
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For customers using R Server 9. The Teradata compute context was discontinued in Machine Learning Server 9. If you have R Server 9. For future compatibility, we recommend modifying existing code to run in other compute contexts, and create a Teradata data source object to work with your data.
For more information about Teradata as a data source, see [RxTeradata].. There are three key components to running RevoScaleR high-performance analytics:.
RevoScaleR provides a variety of data sources and compute contexts that can be used with the high-performance analytics functions. This guide focuses on analyzing Teradata data in-database. That is, the data is located in a Teradata database and the computations are performed at the location of the data. We also consider a second use case: To follow the examples in this guide, you need the following comma-delimited text data files to load into your Teradata database:. These data files each have 10 million rows, and should be put in tables called ccFraud10 and ccFraudScore10 , respectively.
These data files are available online. We recommend that you create a RevoTestDB database to hold the data table. You can find sample scripts for doing this in Appendix I, and online with the sample data files. You need to edit the sample scripts to provide appropriate logon information for your site. Check with your system administrator to see of the data has already been loaded into your database. There is an example later in this guide of loading data into your Teradata data base from R using the rxDataStep function.
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