This course teaches how to build QualityStage parallel jobs that investigate, standardize, match, and consolidate data records. Students will gain experience by building an application that combines customer data from three source systems into a single master customer record.

Audience: • Data Analysts responsible for data quality using QualityStage • Data Quality Architects • Data Cleansing Developers

Brand: UGI - QualityStage; Windows

Event Number: KM213G

Available Languages: English (US),English (UK),French (Canada),German (Germany),Russian (Russia),Japanese (Japan),Chinese (Simplified),Italian (Italy),Polish (Poland),Portuguese (Brazil),French (France),Spanish (Latin America),Spanish (Spain),Portuguese (Portugal),Thai (Thailand),Dutch (The Netherlands),Turkish (Turkey),Romanian (Romania),Czech (Czech Republic),Latvian (Latvia),Lithuanian (Lithuania),Norwegian (Bokml),Swedish (Sweden),Danish (Denmark),Hebrew (Israel),Arabic,Greek (Greece),Korean (Korea),Bulgarian (B

Subjects: Technical

Objectives:

•List the common data quality contaminants

•Describe each of the following processes:

§Investigation

§Standardization

§Match

§Survivorship

•Describe QualityStage architecture

•Describe QualityStage clients and their functions

•Import metadata

•Build and run DataStage/QualityStage jobs, review results

•Build Investigate jobs

•Use Character Discrete, Concatenate, and Word Investigations to analyze data fields

•Describe the Standardize stage

•Identify Rule Sets

•Build jobs using the Standardize stage

•Interpret standardization results

•Investigate unhandled data and patterns

•Build a QualityStage job to identify matching records

•Apply multiple Match passes to increase efficiency

•Interpret and improve match results

•Build a QualityStage Survive job that will consolidate matched records into a single master record

•Build a single job to match data using a Two-Source match

Learn about;
Information Management;
InfoSphere Information Server;
InfoSphere Information Server 11.5.0

Course Detail:

1. Data Quality Issues
• Listing the common data quality contaminants
• Describing data quality processes

2. QualityStage Overview
• Describing QualityStage architecture
• Describing QualityStage clients and their functions

3. Developing with QualityStage
• Importing metadata
• Building DataStage/QualityStage Jobs
• Running jobs
• Reviewing results

4. Investigate
• Building Investigate jobs
• Using Character Discrete, Concatenate, and Word Investigations to analyze data fields
• Reviewing results

5. Standardize
• Describing the Standardize stage
• Identifying Rule Sets
• Building jobs using the Standardize stage
• Interpreting standardize results
• Investigating unhandled data and patterns

6. Match
• Building a QualityStage job to identify matching records
• Applying multiple Match passes to increase efficiency
• Interpreting and improving Match results

7. Survive
• Building a QualityStage survive job that will consolidate matched records into a single master record

8. Two-Source Match
• Building a QualityStage job to match data using a reference match
 

Pre-Requisite Text:

Participants should have:
• Familiarity with the Windows operating system
• Familiarity with a text editor
Helpful, but not required, would be some understanding of elementary statistics principles such as weighted averages and probability.

GTR Start Date / End Date Class ID Location Language Price Enroll
GTR:
Start Date / End Date:
09/24/2019 10:00 EST -
09/27/2019 18:00 EST
764981 Location: NA Virtual - ET Language: English (US) Price: $3280.00 USD Enroll:

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GTR:
Start Date / End Date:
12/17/2019 10:00 EST -
12/20/2019 18:00 EST
764980 Location: NA Virtual - ET Language: English (US) Price: $3280.00 USD Enroll:

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