IBM InfoSphere QualityStage Essentials 11.3

IBM InfoSphere QualityStage Essentials 11.3

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.

Learning Journeys or Training Paths that reference this course:

Information Server – Developer Role

Objetivos

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

Administración y programación bases de datos

Disponible en formato e-learning

Disponible en formato presencial

Disponible en formato a distancia

Acceso al campus

Subvención disponible
A través de Fundae, cumpliendo requisitos.

Duración
20 horas

  • Dificultad 50% 50%
  • Nivel alcanzado 80% 80%

Dirigido a

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

Conocimientos requeridos

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.

Temario

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

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