This lesson develops Data Stores, Snapshots, Profiling, and Data Discovery within Oracle Enterprise Data Quality 12c: Profile, Audit, and Operate Ed 1.
The activity is anchored to a customer-data quality program measuring defects, standardizing records, identifying duplicates, and operationalizing controls, so each design, configuration, analysis, or support decision is tied to a realistic operating outcome.
The instructor will inspect and explain EDQ projects, data stores, connections, snapshots, readers, and staged data in the context of a customer-data quality program measuring defects, standardizing records, identifying duplicates, and operationalizing controls. The demonstration captures commands, configuration, transactions, output, logs, plans, or screenshots that prove both the intended behavior and the control boundary.
Learners complete a controlled exercise covering frequency profiling, completeness, uniqueness, patterns, statistics, and outliers and reference data, lookup checks, business-domain exploration, and issue hypotheses, then introduce a realistic exception involving profile results, defect inventory, critical attributes, and data-quality baseline. They record assumptions, exact steps, expected and observed results, a reversible correction, and the evidence required for operational acceptance.
Acceptance for Data Stores, Snapshots, Profiling, and Data Discovery requires the result to be reproducible, traceable to approved inputs, consistent with the relevant Oracle or platform behavior, and supported by checks for security, data integrity, reliability, performance, and recoverability.
When the result differs from expectation, learners isolate the smallest proven cause across EDQ projects, data stores, connections, snapshots, readers, and staged data, frequency profiling, completeness, uniqueness, patterns, statistics, and outliers, reference data, lookup checks, business-domain exploration, and issue hypotheses; they avoid speculative production changes, preserve evidence, apply the lowest-risk correction, rerun the test, and document prevention.
Complete and defend a validated result for Data Stores, Snapshots, Profiling, and Data Discovery with a reusable implementation record or runbook.