IBM Netezza DBA Training, The IBM Netezza data warehouse appliance — a powerful parallel computing platform — is fully exploited by IBM Netezza Analytics to deliver high-speed, scalable analytics processing.
Netezza Training Course Curriculum
IBM Netezza Overview
Netezza Architecture, Connecting to Netezza
Tables and Database Objects, Data Distribution, Loading and Unloading Tables
Zone Maps, Clustered Base Tables, Materialized Views, Groom,Sequences
Query and System Optimization
Netezza commands and Users
Backup and Restore, Creating user and User management,Query History, Managing Workloads, Managing Events
The IBM Netezza product is comprised of a number of different packages, which together provide a suite of tools that can be used to implement a wide range of analytical activities. The decision regarding which packages need to be installed depends on the functionality required.
The inzaPackageInstaller.sh script simplifies the installation process by automating installation of the various component packages and registering the installed packages, if needed. The script provides the option of installing the entire IBM Netezza Analytic product, including the IBM Netezza Analytics documentation in Adobe PDF format1 , or of installing only selected components.
You can use these commands: nzbackup and nzrestore to back up and restore databases that are enabled for Netezza Analytics. You must, however, do additional steps to back up and restore the Netezza Analyticsspecific data correctly. Netezza Analytics stores metadata about analytic models or matrices in each database; the additional steps are required to ensure that the metadata are correct and consistent after the restore operation. Netezza Analytics stores metadata about models and matrices in a set of tables that are created when you enable a database for Netezza Analytics. Check the consistency of these tables before you do a full or incremental backup of the database. After a database that contains analytic models is restored, the Netezza Analytics metadata tables might contain wrong references, such as names or object IDs, and the definition of the metadata views might be wrong. Wrong references occur especially when you restore the database to another name or on another Netezza system. To fix the wrong metadata, run the metadata_analyze procedure as the administrator. After you restore and re-enable the database, log in to the database and run the following command: call nza..metadata_analyze('mode=restore')
The IBM Netezza Performance Server (NPS®) system’s architecture (Netezza, 2006), is a two-tiered system designed to handle very large queries from multiple users. The first tier is a high-performance Linux symmetric multiprocessing host. The host compiles queries received from business information (BI) applications, and generates query execution plans. It then divides a query into a sequence of sub-tasks, or snippets, which can be executed in parallel, and distributes the snippets to the second tier for execution. The host returns the final results to the requesting application thus providing the programming advantages of appearing to be a traditional database server.
The second tier consists of dozens to hundreds or thousands of Snippet Processing Units (SPUs) operating in parallel. Each SPU is an intelligent query processing and storage node, and consists of a powerful commodity processor, dedicated memory, a disk drive and a field-programmable disk controller with hard-wired logic to manage data flows and process queries at the disk level. The massively parallel, shared-nothing SPU blades provide the performance advantages of massively parallel processors.
Nearly all query processing is done at the SPU level, with each SPU operating on its portion of the database. All operations that easily lend themselves to parallel processing (including record 9 operations, parsing, filtering, projecting, interlocking and logging) are performed by the SPU nodes, which significantly reduces the amount of data moved within the system. Operations on sets of intermediate results, such as sorts, joins and aggregates, are executed primarily on the SPUs, but can also be done on the host, depending on the processing cost of that operation.
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