Semantic Layer vs. Data Catalog: Complementary, Not Competing

Source: Dev.to

Data Catalog

A data catalog is a searchable inventory of your organization’s data assets—think of it as a library card system for data. It tells you what data exists, where it lives, who owns it, and how it flows through your systems.

Key Functions

• Discovery – Find tables, views, files, and dashboards by searching keywords, tags, or owners.
• Lineage – Trace how data moves from source to destination, including every transformation along the way.
• Governance metadata – Track data quality scores, classification (PII, confidential), and compliance status.
• Documentation – Store descriptions of assets, often crowd‑sourced from data producers and consumers.

A data catalog is fundamentally a passive system. You search it, browse it, and read from it. It does not change how queries execute or how metrics are calculated; it simply organizes information about data.

Semantic Layer

A semantic layer defines what data means and how to use it correctly. It is an active system that sits between your raw data and the tools querying it.

Key Functions

• Metric definitions – Revenue, churn rate, active users—calculated the same way everywhere.
• Query translation – Converts business questions into optimized SQL.
• Access enforcement – Row‑level security and column masking applied at query time.
• Documentation – Wikis and labels attached to views and columns.

When a user asks “What was revenue by region?”, the semantic layer translates “revenue” into the correct SQL formula, joins the right tables, applies security filters, and returns the result.

Comparison

Why Both Are Needed

• Catalog without a semantic layer – Users find data but don’t know how to use it correctly. They may write their own revenue formula, leading to inconsistencies across the organization.
• Semantic layer without a catalog – Users get accurate, governed queries for the datasets covered by the layer, but they cannot discover datasets outside the layer. New sources, experimental tables, and raw files remain invisible until manually added.

The most effective architectures integrate both:

1. Discovery & lineage are handled by the catalog across all assets.
2. Meaning, calculation, and governance are handled by the semantic layer for business‑critical datasets.

An integrated system provides a single interface where data discovery and business context exist side by side. You search the catalog to find a dataset, then see its semantic layer definition—metric formulas, documentation, labels, and access policies—alongside catalog metadata (lineage, quality, ownership).

Integrated Example: Dremio

Dremio combines an Open Catalog (built on Apache Polaris, the open‑source Iceberg REST catalog standard) with semantic‑layer features:

• Open Catalog – Inventory of tables, views, sources, and their lineage.
• Virtual datasets (SQL views) – Define business logic and metric calculations.
• Wikis – Document what each dataset and column means.
• Labels – Tag data for governance and discoverability (PII, Finance, Certified).
• FGAC – Enforce row/column security at query time.

AI Agents Benefit

AI agents can leverage this integration directly:

• Use the catalog to navigate available datasets (e.g., “What tables exist in the Sales space?”).
• Use the semantic layer to generate accurate queries (e.g., “What does Revenue mean, and who can see which rows?”).

Removing either piece leaves the AI either blind to available data or generating incorrect SQL.

Quick Self‑Check

Open your current data catalog and pick a business‑critical table:

• Can you see how its key metric is calculated?
• Who can access which rows?
• What do the column names mean in business terms?

If the catalog only shows that the table exists, you’ve identified the gap a semantic layer fills.