How to Evaluate Data Catalog Tools for Your Stack

Most catalog evaluations go wrong before the first demo. Someone opens a spreadsheet, lists ten tools down the left column, features across the top, and starts filling in checkmarks. Two months later the team picks the tool with the most green cells, deploys it, and six months after that nobody's using it because the features that won the spreadsheet battle aren't the ones that matter in practice.

I've been through this three times now — once at a company that picked Collibra, once where we self-hosted DataHub, and once where we ended up not buying anything at all. The pattern that worked wasn't comparing feature lists. It was running a set of concrete tests against each candidate using your own data, in your own environment, with your actual users. This is that test suite.

Start with your metadata, not the vendor's demo dataset

Every tool looks great against its own demo data. Clean schemas, perfect descriptions, a lineage graph that renders beautifully on a 4K monitor during a sales call. Your data doesn't look like that. Your data has 400 tables, 60 of which are deprecated but never dropped, column names like amt_2 and flag_v3, and a lineage path that crosses three orchestration tools.

Before you schedule a single demo, run this against your warehouse:

SELECT
    t.table_schema,
    t.table_name,
    COUNT(c.column_name) AS col_count,
    SUM(CASE WHEN c.column_name ~* '^(id|_id|key|fk_)' THEN 1 ELSE 0 END) AS likely_keys,
    SUM(CASE WHEN c.data_type IN ('timestamp', 'timestamptz', 'date') THEN 1 ELSE 0 END) AS date_cols
FROM information_schema.tables t
JOIN information_schema.columns c
    ON t.table_schema = c.table_schema AND t.table_name = c.table_name
WHERE t.table_schema NOT IN ('pg_catalog', 'information_schema')
  AND t.table_type = 'BASE TABLE'
GROUP BY t.table_schema, t.table_name
ORDER BY col_count DESC;

This gives you your actual table inventory — the thing any catalog will need to ingest. Save it. You'll use it to verify completeness in every tool you test.

Test 1: Ingestion completeness

The first thing to verify isn't whether the UI looks nice. It's whether the catalog found everything.

Point the tool at your warehouse during the trial. Let it run its discovery scan. Then compare what it found against your baseline:

-- Your baseline: tables the catalog should have found
SELECT table_schema, table_name
FROM information_schema.tables
WHERE table_schema NOT IN ('pg_catalog', 'information_schema')
  AND table_type = 'BASE TABLE'
 
EXCEPT
 
-- What the catalog actually ingested (export from the catalog's API or UI)
SELECT schema_name, table_name
FROM catalog_export;

You won't have a literal catalog_export table — you'll need to pull the list from the tool's API or export. The point is the diff. Every tool I've tested has missed something on the first ingestion. Views excluded by default. Materialized views treated as tables or ignored entirely. Schemas the connector didn't have permissions on but didn't error about. A catalog that silently skips 15% of your tables is worse than not having one, because people trust what it shows and don't go looking for what it doesn't.

Atlan and Alation both handled multi-schema Postgres well in my testing. DataHub missed materialized views until I explicitly configured the ingestion recipe. OpenMetadata caught everything but took noticeably longer on large schemas.

Test 2: Metadata coverage depth

Ingesting a table name is the easy part. What you actually need to evaluate is how much useful metadata the tool surfaces without manual enrichment.

After the initial ingestion, pick five tables you know well and check:

• Does it show column-level types correctly, including custom types and arrays?
• Does it detect primary keys and foreign key relationships?
• Does it infer any descriptions from existing COMMENT ON annotations?
• Does it surface freshness — when the table was last modified or loaded?
• Does it detect PII candidates (email, phone, SSN-shaped columns)?

That last one is a real differentiator. Collibra and Atlan both offer PII classification, but their implementations vary from regex-based pattern matching to ML-driven detection. Ask specifically: is the PII classification rule-based, ML-based, or both? Can you add custom patterns for your domain? I've seen a catalog correctly flag user_email but miss contact_addr because the column name didn't match the built-in pattern.

Fastero's catalog runs auto-discovery with privacy classification baked in — it flags PII candidates on ingestion and lets you confirm or dismiss, which saves the manual tagging pass that kills adoption on other tools. If you're a small team without a dedicated steward, that default matters more than the most sophisticated ML classifier you'll never have time to tune.

Test 3: Lineage accuracy

Lineage is the feature that sells catalogs. It's also the feature most likely to disappoint.

Pick a column you can trace by hand — something like monthly_revenue in a reporting table that you know originates from orders.total_amount, passes through a dbt model, and lands in a dashboard. Then check: does the catalog trace that path completely? Where does it lose the thread?

Most catalogs do table-level lineage well. Column-level lineage is where they diverge. Some tools trace it through SQL transformations but not through Python scripts. Some trace it through dbt but not through ad-hoc queries. None of them — not a single one I've tested — trace it through a Jupyter notebook that reads from a warehouse and writes to a CSV that someone uploads to a Google Sheet.

Questions to ask during the eval:

• Does lineage work for views and CTEs, or only materialized tables?
• Does it trace through dbt models using the manifest, or by parsing SQL?
• What happens when a transformation lives in Python or a stored procedure?
• Can it trace lineage across databases — say, from a Postgres source through Snowflake to a Looker dashboard?

If lineage is your primary reason for buying a catalog, spend 80% of your eval time here. Run your own lineage audit before the eval so you know what the ground truth looks like.

Test 4: The search test

This is the one evaluators skip because it feels trivial. It isn't.

Have five people on your team — not the ones running the eval, the ones who'll actually use the catalog day-to-day — sit down with the tool and try to find the answer to a real question they had last week. "Where does the MRR number come from?" "Is there a table with customer churn dates?" "What's the difference between orders and orders_v2?"

Time them. Note whether they found the right answer. Note whether they found it by search, browse, or giving up and asking Slack anyway.

A catalog nobody can search is a catalog nobody uses. I've seen tools with perfect metadata ingestion and a search bar that returns irrelevant results because it indexes table names but not column descriptions, or ranks exact matches below fuzzy ones.

Test 5: The glossary and governance layer

If you're evaluating catalogs because of a compliance requirement — SOC 2, HIPAA, a customer security questionnaire — this is the test that actually matters, and you should run it with your compliance or security lead in the room.

Set up a business glossary term (say, "Monthly Recurring Revenue") and link it to the actual columns that compute it. Then check:

-- After linking the glossary term, can you query which columns are tagged?
-- Most catalog APIs expose something like:
GET /api/v2/glossary/terms/{term_id}/linked_assets
 
-- Verify the linked assets match your known sources:
SELECT column_name, table_name
FROM information_schema.columns
WHERE table_name IN ('subscriptions', 'invoices')
  AND column_name IN ('mrr', 'amount', 'recurring_amount');

What you're testing isn't whether the glossary feature exists — every catalog has one. You're testing whether linking a term to an asset is fast enough that someone will actually do it for 50 terms, or whether it takes so many clicks that the glossary will be half-populated forever.

Collibra's governance layer is the deepest here — stewardship workflows, approval chains, audit trails. For a 200-person data org with regulatory obligations, that depth earns the price. For a 10-person team, it's overhead that slows down the people who would otherwise just tag things and move on.

The traps

Trap 1: Evaluating on features you won't use for 18 months. Every enterprise catalog has a governance module, a data marketplace, a collaboration layer, API access management. If you're buying because you need discovery and lineage today, don't let a feature you might need in 2028 swing the decision. You'll switch tools before then anyway.

Trap 2: Ignoring the connector gap. A catalog supports "200+ connectors" but doesn't have one for your CRM's API, your event tracking system, or the internal microservice that writes to a Kafka topic. Ask specifically about every source you need to catalog, including SaaS tools and internal systems. The number on the marketing page is meaningless if your three most important sources aren't on the list.

Trap 3: Confusing the trial with production. Trial environments get white-glove support, fast connector setup, and a solutions engineer who'll configure things for you. Ask what happens post-sale. Who maintains the ingestion pipelines when a connector breaks? How fast is support response for non-enterprise tiers?

Trap 4: Skipping the "what if we stop using it" test. Can you export your metadata — descriptions, tags, glossary terms — in a format another tool can import? Or does everything you invest in enriching the catalog become locked inside a proprietary system? DataHub and OpenMetadata both store metadata in open formats. Not all commercial tools do.

Where Fastero fits

Fastero isn't trying to be Collibra. If you need enterprise governance workflows with approval chains and regulatory audit trails, buy an enterprise governance tool.

What Fastero's catalog does well is the 80% that small-to-mid teams actually use: auto-discovery across 21+ database connectors and 13+ SaaS integrations, a glossary you can populate without a stewardship committee, privacy classification that flags PII on ingestion, and external adapters for DataHub, OpenMetadata, and Unity Catalog so you're not locked in. If you already run DataHub or OpenMetadata, Fastero syncs with them rather than replacing them — your catalog stays where it is, and Fastero reads from it.

Pair that with column-level lineage, drift detection, and automated profiling, and you've got a catalog that stays accurate without a dedicated catalog maintainer. That's the part that actually determines adoption.

The evaluation checklist

Run these tests against every tool you're considering, including the free tier of open-source options:

1. Ingestion completeness: diff against information_schema. Anything missing?
2. Metadata depth: pick 5 tables, check types, keys, freshness, PII detection.
3. Lineage accuracy: trace one column end-to-end. Where does it break?
4. Search quality: 5 real users, 5 real questions, timed.
5. Glossary usability: link 10 terms to assets. How long did it take?
6. Export capability: can you get your enriched metadata back out?
7. Connector coverage: verify every source you need, not just the top 3.

Skip the feature matrix. Run the tests. The tool that wins on your data, with your team, in your environment is the right one — regardless of how many checkmarks it has on a comparison page.

If you're starting the eval and want a baseline of what your schemas look like before any tool touches them, profiling a new dataset in under 5 minutes and auditing data freshness across your tables will give you the ground truth to test against.

Try Fastero free — auto-discover your tables, flag PII, and get column-level lineage without a six-figure contract. No credit card required.