What you will learn
Data profiling, Provenance, PII minimization, Versioning, Reconciliation.
Engineering the capability
A prototype built on hand-picked documents may hide the customer’s real data problems. Profile completeness, duplication, format variation, language, freshness, and permissions. Sample across sources and business cases instead of only the cleanest folder.
Maintain provenance from source record through transformations to indexed or derived data. Decide which fields are needed and avoid copying sensitive fields without a purpose. An extraction failure should create a visible rejected record, not an empty passage that silently enters the index.
Plan synchronization and deletion from the beginning. Compare counts and identifiers between source and target, account for rejected records, and make updates idempotent. A migration is incomplete if an old source deletion leaves searchable copies behind. Document the operational owner of data corrections.
Worked case
The source contains 500 records, but the index has 470. “About the same size” is insufficient. A reconciliation report should explain all 30 missing records: unsupported format, missing permission metadata, duplicate identity, or processing error. Each category needs a decision before the index is trusted.
Put it into practice
Continue with the next lab: produce a data-readiness report. Build the artifact, record the failure cases, and explain the tradeoff before moving on.