Category Archives: General

Securing (and sharing) password information in Sqoop jobs

Sqoop is a utility that allows you to move data from a relational database system to an HDFS file system (or export from Hadoop to RDBMS!).  One of the things to keep in mind as you start building Sqoop jobs … Continue reading

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Setting custom properties for Hive databases

Hive supports the concept of a database as a logical collection of objects stored in separate catalogs or namespaces. One neat thing that you can do with Hive is add extended properties to your database that are displayed when describing … Continue reading

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Enabling Netezza Analytics for use in a database

Many of Netezza’s analytic functions require the presence of some metadata tables/views in order to work.  These tables/views are created when you initialize the analytic libraries in a particular database. SYSTEM(ADMIN)=> call nza..kmeans(‘intable=t1,id=int1,k=10,model=kmeans,outtable=out,maxiter=5’); ERROR:  The metadata tables are not initialized. … Continue reading

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Dropping a Netezza Analytics model

The result of most of Netezza’s analytic functions are a series of tables.  The tables produced vary from function to function.  We schedule jobs to run the analytic functions and want to reuse the same model name so that our … Continue reading

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Using nz_zonemap to visualize Netezza’s zone map effectiveness

Netezza has a lot of tools in /nz/support/contrib/bin that make life for the NZ DBA much, MUCH easier.  One such tool is nz_zonemap. Zone maps are how the system keeps track of what records exist in a particular extent (3MB … Continue reading

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The relationship between groom and nzbackup

There are three basic functions that every Netezza DBA must perform regularly: Ensure statistics are up to date Groom your tables Backup your production databases Let’s focus on groom and its dependency on nzbackup.  I recently ran into an issue … Continue reading

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Netezza in-database modeling with SPSS Modeler 14.2: K-Means 35x faster

Leveraging Netezza’s in-database analytic capabilities can significantly reduce the amount of time required to execute SPSS streams.  By pushing the analytics to the data, we eliminate the need to pull the data out of the table and onto our SPSS … Continue reading

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