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.
Data Cleansing Developers
List the common data quality contaminants
Describe each of the following processes:
Describe QualityStage architecture
Describe QualityStage clients and their functions
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
Participants should have:
Familiarity with a text editor
Helpful, but not required, would be some understanding of elementary statistics principles such as weighted averages and probability.
1. Data Quality Issues
Building a QualityStage job to match data using a reference match
If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. It's a cost effective option.
Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.
|Live Virtual||Live Virtual||4||3000 £3000||2019-09-23|
|Live Virtual||Live Virtual||4||3000 £3000||2019-10-29|
|Live Virtual||Live Virtual||4||3000 £3000||2019-11-25|
|Live Virtual||Live Virtual||4||3000 £3000||2019-12-16|
|Live Virtual||Live Virtual||4||3000 £3000||2020-01-28|