Overview
| Course code | 1W352 | Skill level | Advanced |
|---|---|---|---|
| Duration | 40.0 hours | Delivery type | Web Based Training |
| Course type | Public only | ||
| Public price | USD $1,500.00 plus tax |
Note: This is a self-paced online course. This course usually requires 40 hours to complete. Once you receive your access information, you will have access to this course for 1 year.
This course is designed to give you an in depth knowledge of the components of InfoSphere Warehouse. The course will start with the SQL Warehousing Tool where, using the Design Studio, you will create both data and control flows and then deploy your work to the Administration Console. That is followed by defining OLAP metadata after which you will deploy a cube to a Cubing Services server. Finally, you will create mining flows that will invoke clustering, prediction, and association mining algorithms as well as mining flows that do unstructured text analysis. This course is for both of you that are using IBM InfoSphere Warehouse and IBM InfoSphere Warehouse on System z.
Please do not make travel arrangements for this course. After you receive confirmation that you are enrolled, you will be sent further instructions to access the course materials.
Course Materials
The course materials address InfoSphere Warehouse 9.7.
Hands-On Labs
Fourteen labs demos 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.
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Training Paths that reference this course are:
Audience
This advanced course is intended for you who will be administering one or more of the components that come with IBM InfoSphere Warehouse or IBM InfoSphere Warehouse on System z.
Skills taught
- 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
Course outline
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
- List the SQL and DataStage integration points
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 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
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
- istinguish 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
Agenda
1WA52
- Unit 1: InfoSphere Warehouse Components Overview
- Unit 2: Design Studio
- Exercise for Design Studio
- Unit 3: Data Modeling
- Exercise for Data Modeling
- Unit 4: SQL Warehouse Tool
- Exercise for SQL Warehouse Tool
- Unit 5: Control Flows
- Exercise for Control Flows
- Unit 6: Administration Console
- Exercise for Administration Console
1WB52
- Unit 7: Introduction to Cubing Services
- Unit 8: Cubing Services Metadata
- Exercise for Cubing Services Metadata
- Unit 9: Cubing Services Administration
- Exercise for Cubing Services Administration
1WC52
- Unit 10: Cubing Services Optimization Advisor
- Exercise for Optimization Advisor
- Unit 11: A Data Mining Foundation
- Unit 12: An Introduction to InfoSphere Intelligent Miner
- Unit 13: InfoSphere Intelligent Miner Supported Mining Techniques
- Topic 1: Clustering Functions
- Exercise for Clustering
- Topic 2:Predictive Models
- Unit 13: InfoSphere Intelligent Miner Supported Mining Techniques (Continued)
- Exercise for Prediction
- Topic 3: Associations and Sequential Rule
- Exercise for Associations and Sequential Rule
- Unit 14: Unstructured Text Analysis
Remarks
Special Note for IBM Business Partners authorized to remarket IBM IT Education Services public classes and on-site classes: This course is excluded from the IBM Technical Training Services for Public Education Program and the IBM Training Services for Enterprise - on-site Education Program for IBM Business Partners.
Machine requirements
HW/SW CONFIGURATION
The minimum hardware and software required to launch the course are:
- Reliable HIGH-SPEED INTERNET connection (min 200 kbps up and down)
- Windows 2000 or XP or Vista
- Computer with soundcard
- Headset or computer speakers
- Internet Explorer 5.01 or later, or Firefox 1.0 or later
Network Speed Test
http://clpext.moppssc.com/index.php?option=com_wrapper&view=wrapper&Itemid=8
User: clp
Pass: ibmeduc
For example, a speed test against the server with a slow connection of 140 Kbps download and 28 Kbps upload took 14 minutes to load a 30min recording before the video began. Extrapolate from this result to estimate approximately how fast your network internet access would be.
High-speed broadband internet access is the recommended configuration for this course.
Keyboard Configuration
If you use a different character keyboard, you may experience errors when entering passwords. If possible, change your language/country settings for your keyboard to USA, which allows you to enter characters as in a QWERTY keyboard.