Building vs Buying: Catalog Trade offs and Alternatives

The Core Decision: Should you build a data catalog in house or adopt an existing solution? This choice depends on scale, complexity, and how critical discovery is to your data platform. Manual documentation (wikis, spreadsheets, internal docs) works when you have one warehouse and fewer than 50 tables. Everyone knows where the data lives and what it means. The moment you add a second warehouse or grow past 100 tables, manual approaches break down. Documentation becomes stale within weeks. Nobody knows which tables are deprecated. Lineage lives in people's heads. A catalog solves this by automating discovery, tracking lineage through actual job execution, and staying current through continuous ingestion. The trade off is upfront investment: you need connectors to every system, standardized event formats, and operational overhead to keep ingestion pipelines healthy. Catalog vs Semantic Layer: Some teams confuse a catalog with a semantic layer or virtualized data access system. A semantic layer sits on the query path, enforcing a single logical schema over underlying data sources. Every query goes through it. This provides strong guarantees: all users see the blessed definitions and access patterns. A catalog is lighter weight. It does not intercept queries. It discovers, documents, and recommends, but analysts can still query any table directly. This makes adoption easier and less risky to core data processing. The trade off is weaker enforcement: a catalog cannot guarantee that everyone uses the blessed datasets, only encourage it through search ranking and certification badges. Choose a semantic layer when you need strict governance over a specific domain, like financial reporting where every query must use approved metric definitions. Choose a catalog when you need broad discovery across a heterogeneous platform where different teams have different needs. Warehouse Schema Views as Implicit Catalog: Some companies try to use their warehouse's information schema views as an implicit catalog. You can query INFORMATION_SCHEMA.TABLES to list all tables and INFORMATION_SCHEMA.COLUMNS for schema. For a small, warehouse centric team, this is simple and requires zero additional infrastructure. This approach breaks at moderate scale. First, it only covers data in that warehouse. You have no visibility into data lakes, streaming topics, or external APIs. Second, you get no cross system lineage. You can't see that a warehouse table feeds a dashboard or that a Kafka topic feeds a warehouse table. Third, you lose governance features like certification workflows, usage tracking, and search ranking. The trade off is operational simplicity versus platform visibility. Use schema views when you truly have a single warehouse and under 500 tables. Beyond that, the lack of lineage and cross system discovery becomes painful. Freshness vs Cost: Real time metadata (under 1 second latency) sounds appealing but is expensive. You need low latency event buses, streaming processors, and cache invalidation at every layer. For most use cases, this is overkill. For search and discovery, 1 to 5 minute delays are fine. Analysts don't need to find a table within seconds of its creation. For impact analysis before deploying a schema change, 5 minute delays are acceptable. You're planning changes hours or days in advance. The one case where sub second freshness might matter is automated systems that deploy changes based on catalog metadata. For example, a service that auto scales consumers based on lineage. Even then, you can design with eventual consistency in mind: make decisions idempotent, add safety delays, or use circuit breakers. Most production catalogs accept minute level delays, which simplifies architecture and cuts infrastructure costs by 5x to 10x compared to real time ingestion.