Metadata & Catalog Quickstart Kit

Purpose

This Quickstart Kit helps teams stand up a pragmatic, useful metadata catalog and basic lineage tracking that people will actually use. It removes friction by focusing on the smallest set of fields, roles, and steps that deliver discoverability, explainability, and trust — then suggests lightweight automation to scale. Preserve simplicity: deliver value early, iterate later.

What this kit contains

• Recommended metadata fields and minimal catalog schema
• Three example business-glossary entries
• Lightweight lineage / provenance capture approach
• Roles and responsibilities for operationalizing the catalog
• Onboarding checklist for the first 10 datasets (practical acceptance criteria)
• Automation and integration suggestions
• Sample queries and API examples you can adapt

Recommended metadata fields (minimal useful set)

Start with these fields for each dataset (dataset-level metadata). Add field-level metadata for the most-used datasets or sensitive fields.

• DatasetID / Registry Key — stable unique identifier
• Title / Name — concise, searchable name
• Description — why the dataset exists and typical uses
• Owner — accountable person or role (business owner)
• Steward — day-to-day contact for questions
• Business Glossary Term(s) — link to canonical terms
• Source Location — physical/virtual location (DB, bucket, topic)
• Schema / Sample Columns — column list, types, key columns
• Refresh Cadence — batch / stream frequency
• Row Count / Size — last profiled counts
• Data Quality Summary — a short score or status and last profile date
• Upstream Sources — simple list of immediate upstream datasets
• Transformation Summary — plain-language note of major transforms
• Sensitivity / Access — classification (Public / Internal / Confidential) and access owner
• Last Updated — metadata timestamp

Example glossary entries (starter)

Lightweight lineage / provenance approach

Capture two levels of lineage initially: dataset-level and critical field-level. Keep provenance records human‑readable and machine‑parsable. For each dataset, record:

• Immediate upstream dataset(s)
• Extraction or ingest query / job name
• Primary transformation steps in plain language (e.g., "normalize customer_id; drop PII columns; aggregate daily totals")
• Last successful run timestamp
• Owner / responsible person

Simple JSON provenance example (store in your catalog's metadata field):

{ "dataset_id":"orders_daily", "upstreams":["orders_raw"], "transform_job":"etl_orders_v2", "summary":"rename columns, convert timezone, filter test orders", "last_run":"2026-08-01T02:15:00Z" }

Roles & responsibilities (practical definitions)

• Data Owner — accountable for dataset quality, access decisions, and business meaning.
• Data Steward — operational contact, maintains catalog entry, answers questions.
• Data Engineer — implements ingestion, emits lineage metadata, automates profiles.
• Catalog Manager — runs the catalog platform, manages templates and onboarding workflow.
• Analyst / Consumer — validates dataset fitness for use and reports issues back to steward.

Onboarding checklist for the first 10 datasets (acceptance-focused)

1. Confirm Owner & Steward. Acceptance: owner listed and has acknowledged responsibility.
2. Create catalog entry with recommended metadata fields populated at least to Title, Description, Owner, Source Location, Refresh Cadence.
3. Map at least one glossary term. Acceptance: dataset links to canonical business term.
4. Attach a sample query or example SQL that returns 5 sample rows. Acceptance: sample runs and shows intended columns.
5. Capture immediate upstream(s) and provide a one-line transformation summary. Acceptance: upstream recorded and summary added.
6. Run an automatic data profile (row count, null% on key columns, distinct on join keys). Acceptance: profile snapshot attached.
7. Set sensitivity/access classification and add an access owner. Acceptance: access level set and owner agrees to review requests.
8. Schedule a monitoring job or health check (basic alert on missing refresh). Acceptance: health check configured or on roadmap with owner.
9. Publish the dataset entry and announce it to a shared channel (email or slack) for initial consumers. Acceptance: announcement made and feedback channel established.
10. Record improvement backlog items (if any) and assign one quick win to complete within 2 sprints. Acceptance: backlog created with at least one assigned task.

Automation suggestions

Automate the repetitive pieces so stewards focus on meaning, not form:

• Harvest schema and row counts from databases or data lake using connectors (Airflow, dbt hooks, ingestion jobs) to populate Source Location, Schema, and Row Count.
• Emit lineage from ETL jobs: add a metadata step that writes a small JSON provenance blob to the catalog after successful runs.
• Schedule periodic profiling jobs to compute null rates and simple data-quality metrics and push results into the catalog's Data Quality Summary field.
• Use tags for business domains so search and recommendations work early (e.g., finance, ops, marketing).

Sample queries and examples

Use these as starting points — adapt to your platform.

• Row counts by table (SQL)

  SELECT table_name, row_estimate FROM information_schema.tables WHERE table_schema='analytics' ORDER BY row_estimate DESC LIMIT 50;

• Simple column profile (SQL)

  SELECT COUNT(*) AS rows, SUM(CASE WHEN customer_id IS NULL THEN 1 ELSE 0 END) AS null_customer_id, COUNT(DISTINCT customer_id) AS distinct_customers FROM analytics.orders_daily;

• Catalog update via API (pseudo-curl)

  curl -X POST https://catalog.example/api/datasets -H 'Authorization: Bearer TOKEN' -H 'Content-Type: application/json' -d '{"dataset_id":"orders_daily","title":"Orders (daily)","owner":"alice@example.com"}'

Minimal catalog schema example (JSON stored in a metadata column)

{ "dataset_id":"orders_daily", "title":"Orders (daily)", "owner":"alice@example.com", "steward":"data-team@example.com", "source":"s3://data/warehouse/orders/", "schema":[{"name":"order_id","type":"string"},{"name":"order_date","type":"timestamp"}], "refresh":"daily", "sensitivity":"internal", "upstreams":["orders_raw"], "last_profile":"2026-08-01" }

How to use this kit

Run the onboarding checklist on a pilot set of 5–10 datasets that matter to a small team. Measure success by: faster discovery (time to find a dataset), fewer “what is this column?” questions, one working lineage visualization, and one automated profile feeding the catalog. Iterate — add field-level metadata and more automation once people start using and trusting the catalog.

Includes recommended metadata fields, example glossary entries, lightweight lineage capture approach, roles and responsibilities, onboarding checklist for first 10 datasets, and automation suggestions. Also includes sample queries for common catalog tools.