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.
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.
Data Analysts responsible for data quality using QualityStage Data Quality Architects Data Cleansing Developers
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 matchLearn aboutCloud & Data PlatformData and AIUnified Governance & Integration MarketDataOps Portfolio