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DW352MY - InfoSphere Warehouse 9 Components

Course outline

5 days
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Duration

5 days

Course Description

This course is designed to give the student in-depth knowledge of the components of InfoSphere Warehouse. The course will start with the SQL Warehousing Tool where, using the Design Studio, the student will create both data and control flows and then deploy their work to the Warehouse Console. (There is also a two-day course, InfoSphere Warehouse 9 - SQL Warehouse Tool and Administrative Console (DWA52),that covers this same material.) That is followed by defining OLAP metadata after which the student will deploy a cube to a Cubing Services server. (There is also a one-day course, InfoSphere Warehouse 9 - Cubing Services (DWB52), that covers this same material.) Finally, the student will create mining flows that will invoke clustering, prediction, and association mining algorithms as well as mining flows that do unstructured text analysis. (There is also a two-day course, InfoSphere Warehouse 9 - Data Mining and Unstructured Text Analysis (DWC52), that covers this same material.) This course is is appropriate for students who are using InfoSphere Warehouse within the IBM Smart Analytics system and for students that are using IBM InfoSphere Warehouse and IBM InfoSphere Warehouse on System z independently.

Course Materials

The course materials address InfoSphere Warehouse 9.7.

Hands-On Labs

Fourteen labs are included to address InfoSphere Warehouse 9.7.

Training Path

This course is part of an IBM Training Path. Taking this course in the recommended sequence allows you to maximize the benefits from your education.

Audience

The course is intended for students who will be administering one or more of the components that come with IBM InfoSphere Warehouse or IBM InfoSphere Warehouse on System z.

This course is also appropriate for customers who have acquired the IBM Smart Analytics System.

Pre-requisites

It is strongly suggested that the student be familiar with DB2.

Course Objective

  • Describe the components of InfoSphere Warehouse
  • Use the Design Studio to physically model tables that will be used as sources and targets for the SQL Warehousing Tool
  • Use the Design Studio to create data flows in order to move data within DB2
  • Combine data flows into control flows and deploy the control flows into the SQL Warehousing Tool runtime environment
  • Describe the different data mining algorithms supported by InfoSphere Intelligent Miner
  • Create mining flows that will create data mining models and score those models against new data
  • Use Intelligent Miner Visualizers to view data mining results
  • Use Cubing Services to create MQTs to enhance some OLAP tools' performance
  • Extract data from an unstructured text field in order to enhance a data mining run

Key topics

InfoSphere Warehouse Components Overview

  • Describe the InfoSphere Warehouse architecture
  • List the components of InfoSphere Warehouse
  • List the different InfoSphere Warehouse editions

Design Studio

  • Describe the Eclipse platform
  • Explain how to use perspectives in the Design Studio
  • List the components of the Business Intelligence perspective
  • Explain how to customize Design Studio perspectives

Physical Data Modeling

  • Explain how to create physical data models from scratch or by reverse engineering
  • Describe how to validate the model using Analyze Model
  • Generate a DDL script to deploy your model to a database
  • Compare objects in the project to the database to generate a delta DDL script

SQL Warehousing Tool

  • Describe the use of the SQL Warehousing Tool
  • List the SQL Warehousing Tool components
  • Explain the use of data flow: Operators, Ports, and Connectors
  • List the components of a data warehouse project
  • Describe the use of variables in the SQL Warehousing Tool
  • Define a data station operator
  • Describe an Execution Plan Graph

Control Flows

  • Define a control flow
  • List the components of a control flow
  • Describe control flow ports
  • Explain how to use the control flow iterator
  • Describe the integration between SQW and DataStage
  • Explain how to import a DataStage parallel job to run in SQW

Administration Console

  • Describe the SQL Warehousing Tool deployment process
  • Explain the use of application profiles
  • List the functions of the Administration Console
  • Explain the role based security used by the Administration Console

Introduction to Cubing Services

  • Define OLAP and its position with Business Intelligence
  • Contrast different types of OLAP
  • State the benefits of OLAP
  • List the components of Cubing Services
  • Define Summary Tables as used by Cubing Services
  • Describe how Cubing Services exploits Summary Tables
  • State the benefits of using Cubing Services

Cubing Services Metadata

  • Explain how OLAP metadata is:
  • Imported into the Design Studio
  • Deployed to the InfoSphere Warehouse repository
  • List the main tasks when defining a cube model
  • Describe dimensions, levels and hierarchies
  • List the different hierarchy types
  • Define advanced measures for a cube model

Cubing Services Security and Virtual Cubes

  • Describe Cubing Services concept of virtual cubes
  • Explain how to create a virtual cube
  • Give examples of where a virtual cube is appropriate
  • Explain how to merge members in a virtual cube
  • List the objects that make up the Cubing Services security model
  • Describe the Cubing Services security model lifecycle
  • Explain the resolution when there are conflicting access and deny policies for a user

Cubing Services Administration

  • Describe the Cubing Services architecture
  • List some examples of cube server configuration parameters
  • List the cube deployment criteria
  • Describe the action that takes place when queries are made
  • against a cube server
  • Describe some tools used to query a cube via a Cubing Services cube server

Cubing Services Optimization Advisor

  • State the benefits of using Summary Tables
  • Compare SQL used to create different Summary Tables
  • Use Optimization Advisor to generate Summary Table scripts

A Data Mining Foundation

  • Define data mining
  • Distinguish between verification-driven and discovery-driven analysis
  • Discuss where data mining can be applied
  • Describe the key elements for a successful data mining project
  • Describe the purposes and uses of a data mining process
  • State six steps in a data mining process

An Introduction to InfoSphere Intelligent Miner

  • Describe the components of InfoSphere Intelligent Miner
  • List the different model types supported by InfoSphere Intelligent Miner Modeling
  • Describe how InfoSphere Intelligent Miner Scoring is used
  • Explain how to inspect your data using different distributions: Univariate, Bivariate, and Multivariate
  • Describe how to execute a mining flow
  • Discuss how to generate a Java class from a mining flow

InfoSphere Intelligent Miner Supported Mining Techniques

  • Describe the Cluster function used in InfoSphere Intelligent Miner Modeling
  • Describe the Classification function used in InfoSphere Intelligent Miner Modeling
  • Describe the Regression function used in InfoSphere Intelligent Miner Modeling
  • Describe the Associations function used in InfoSphere Intelligent Miner Modeling
  • Describe the Sequential Rule function used in InfoSphere Intelligent Miner modeling
  • Describe the Time Series Analyisi function used in InfoSphere Intelligent Miner modeling

Unstructured Text Analytics

  • Describe the regular expression extraction capabilities of InfoSphere Warehouse
  • Describe how the frequent terms analysis capabilities of the Design Studio can aid in creating a dictionary
  • Describe how list base information extraction can be used to enhance a data mining run

Programme scope, delivery arrangements and suitability are confirmed during enquiry.