Practise vocabulary for technical vs. business metadata, operational metadata, schema metadata, metadata-driven architecture, and data classification labels (PII, confidential, public).
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Technical metadata in a data catalog includes:
Technical metadata is harvested automatically by catalog crawlers: column names, data types, nullability, partitioning, row counts, table size, last modified timestamp. Business metadata (descriptions, glossary links, ownership) must be added by humans. Both are needed for a complete picture.
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Operational metadata about a dataset refers to:
Operational metadata tells you: "Is this data fresh? When was the pipeline last run? Did the last run succeed? How many rows were loaded?" Modern catalogs surface operational metadata alongside technical and business metadata, so analysts can judge data freshness before using it.
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A "metadata-driven architecture" means:
Example: a metadata-driven ingestion framework reads configuration from a metadata store ("ingest table X from source Y, using schema Z, with incremental key updated_at") and generates the pipeline automatically. Adding a new table means updating metadata, not writing new code — dramatically reducing engineering effort.
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PII (Personally Identifiable Information) as a data classification label means:
PII classification in catalogs drives automated governance: PII-tagged columns trigger masking in non-production environments, restrict access to authorised roles, are included in right-to-erasure workflows, and must be documented in data processing agreements. Without PII tagging, compliance is manual and error-prone.
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Data freshness metadata in a catalog context refers to:
Freshness metadata example: "orders_daily was last refreshed 3 hours ago; expected refresh is daily at 06:00 UTC; last 7 days: 6/7 on-time." A consumer planning a report can see immediately whether the data is stale. Catalogs that surface freshness prevent analysts from presenting yesterday's data as today's.