Failure Modes: When Discovery Breaks Down

Metadata Staleness and Trust Erosion: The most insidious failure mode is stale metadata. Schemas change, tables get deprecated, pipelines break, but the catalog shows outdated information. An analyst discovers a table marked as "refreshed hourly" that actually stopped updating three days ago. After hitting this a few times, users stop trusting the catalog entirely and go back to Slack threads. At scale, you need near real time ingestion from schema registries, job schedulers, and data quality systems. A common Service Level Objective (SLO) is metadata updates visible within 2 to 5 minutes for critical systems. Miss this, and you are showing users a fantasy version of your data landscape. Broken Lineage and Impact Blindness: Lineage is critical for impact analysis and regulatory audits. Missing or incorrect lineage means you cannot answer, "Which dashboards break if I deprecate this column?" Three edge cases cause lineage gaps: First, ad hoc transformations that bypass orchestration tools leave no trace. An analyst runs a manual SQL script to create a derivative table, and the catalog never learns about it. Second, polyglot pipelines across Spark, Airflow, and custom scripts have inconsistent lineage extraction. Third, dynamic query generation makes parsing impossible. The consequence: you deprecate a column, and three weeks later a critical executive dashboard breaks because it had an undocumented dependency. Or worse, a compliance audit asks for downstream impact of customer data, and you cannot provide complete lineage. Security Leaks Through Metadata: Even metadata can leak sensitive information. If the catalog shows column names like credit_card_number or ssn to unauthorized users, you have violated privacy policies. Edge cases include caching search results in clients after access is revoked, replicated catalogs in multiple regions with inconsistent policies, and AI classification that misses PII fields. The fix requires metadata level authorization on every search and browse request, propagating identity context when redirecting to query tools, and careful cache invalidation when permissions change. Search Relevance Collapse: Poor ranking silently degrades the user experience. When the catalog grows from 5,000 to 50,000 datasets, naive text matching returns hundreds of results for common terms. The correct table is buried on page five, so users either pick the wrong one or give up. Handling synonyms and abbreviations is particularly hard. "DAU" means daily active users in the growth team and daily active uploads in the data platform team. The system needs context: which team is the user on? What did they query recently? Which datasets does their team use most? Performance Bottlenecks at Scale: Full table scans for data profiling on every schema change can exhaust cluster resources. Brute force re indexing of the entire catalog on every update causes multi second search latencies. As the catalog grows to millions of entities, naive joins for lineage queries hit 10 plus seconds and break the interactive experience. The solution is incremental updates: profile only changed tables, re index only modified entities, and materialize common lineage paths. But implementing this correctly requires careful bookkeeping of what changed and when.