How to learn Data Catalog And Metadata for Documentation in Data Engineer for free

Why this matters

As a Data Engineer, you build and move data. Without a clear data catalog and consistent metadata, people can’t find, trust, or safely use what you build. Good metadata reduces support requests, prevents broken pipelines, and speeds up onboarding.

Real task: publish a new dataset (name, description, owner, schema, freshness, sensitivity).
Real task: map lineage so analysts know what upstream tables feed a dashboard.
Real task: mark PII columns and define access levels for compliance.
Real task: record data quality checks and their last results.
Real task: deprecate a dataset with a sunset date and replacement pointer.

Concept explained simply

A data catalog is like a library catalog for your data assets. Metadata is the information card about each asset: what it is, who owns it, how to use it, where it came from, and how fresh and reliable it is.

Mental model

Think "passport + map + care label":

Passport: identity (name, description, owner, tags).
Map: lineage and dependencies (upstream/downstream).
Care label: quality checks, freshness, retention, sensitivity, SLA.

Core types of metadata

Technical: schemas, data types, storage location, partitions, indexes.
Business: friendly names, definitions, glossary terms, KPIs, units.
Operational: freshness, load schedule, run duration, last success, SLA.
Lineage: upstream inputs, downstream consumers, transform jobs.
Governance: owners, stewards, sensitivity/PII tags, retention, access level.
Quality: tests/expectations, last run results, anomaly notes.

Tip: minimum viable metadata (MVM)

Start with Name, Description, Owner, Status, Schema, Freshness, Sensitivity. Expand later.

Worked examples

Example 1: Dataset passport (minimal YAML)

dataset: mart.sales.fact_orders
name: Fact Orders
description: One row per finalized order with revenue and cost aggregates.
owner: data-team@sales
status: active
freshness: <= 2h from source
sensitivity: contains_pii: false
schema:
  - column: order_id
    type: STRING
    description: Unique order identifier
    constraints: primary_key
  - column: order_date
    type: DATE
    description: Order booking date (company calendar)
  - column: revenue
    type: DECIMAL(18,2)
    description: Net revenue in USD
  - column: channel
    type: STRING
    description: Sales channel (web, retail, partner)

Why this works

It covers identity, ownership, usability, and key schema details. It’s enough for discovery and first-use.

Example 2: Lineage snippet (JSON)

{
  "asset": "mart.sales.fact_orders",
  "upstream": [
    "raw.sales.orders_raw",
    "dim.calendar",
    "dim.channel"
  ],
  "downstream": [
    "dashboards/revenue_overview",
    "mart.sales.customer_ltv"
  ],
  "transform_jobs": ["jobs/build_fact_orders_daily"]
}

Why lineage matters

When an upstream breaks, you instantly know who to notify. When changing a column, you know which downstream assets to update.

Example 3: Quality expectations and results

quality:
  expectations:
    - name: not_null_order_id
      check: order_id is not null
      severity: high
    - name: revenue_non_negative
      check: revenue >= 0
      severity: medium
    - name: freshness_within_2h
      check: loaded_at within 2 hours of now
      severity: high
  last_run:
    timestamp: 2026-01-07T08:15:00Z
    failures: [ ]
    notes: "All checks passed"

How to use

Store expectations alongside the dataset entry and surface the last_run status in the catalog UI.

How to document a dataset quickly

Identify the asset: stable name, layer (raw/stage/mart), domain (e.g., sales), and purpose.
Capture ownership: team email, on-call channel, and a business contact.
Summarize: 1–2 sentence plain-English description with scope and grain.
Schema essentials: column names, types, key constraints, and business-friendly meanings.
Operational bits: schedule, freshness SLA, last successful run.
Governance tags: PII flag, access level, retention.
Lineage: upstream inputs and known downstreams.
Quality: a few high-value checks and last results.

Time saver: templates

Use a standard template for all datasets so people know where to find each detail.

Exercises

Complete both exercises below. Aim for clarity and consistency over perfection.

Exercise ex1: Minimal metadata spec

Create a minimal metadata entry for a dataset that tracks daily active users (DAU) by app version.

Asset name: choose a clear layer/path (e.g., mart.product.app_dau).
Provide: name, description, owner, status, freshness, sensitivity.
List 4–6 columns with types and short descriptions.

Exercise ex2: Trace lineage

You ingest events into raw.events. You aggregate to stage.events_daily and then to mart.product.app_dau. Document lineage and one quality expectation.

List upstream and downstream for each layer.
Add one freshness SLA for mart.product.app_dau.

Self-check checklist

☐ Every asset has an owner and a working contact.
☐ Descriptions use plain language and define the grain.
☐ Schema includes key columns and meanings.
☐ Freshness SLA is stated and realistic.
☐ Sensitivity/PII tags are present where needed.
☐ Upstream/downstream are listed for at least one hop.

Common mistakes and self-check

Too technical, no business meaning: If a non-engineer can’t tell what it is, add a plain-English description.
No owner: Unowned data becomes stale. Always set a team and contact.
Stale freshness claims: If SLA is missed, update it or fix the pipeline.
Missing PII flags: Classify sensitive columns early to avoid access issues.
Hidden lineage: Changes break downstream users. Record upstream/downstream.
Huge docs, no template: Keep it consistent and scannable.

Self-audit in 5 minutes

Open three popular datasets and confirm owner, description, freshness, and lineage exist.
If any are missing, create quick stubs and schedule a follow-up to refine.

Practical projects

Catalog Sprint: Pick 10 top-used datasets. Apply the minimal template to each and add 1–2 quality checks. Present before/after impact.
Lineage Map: For one domain (e.g., sales), draw asset lineage from raw to dashboards. Capture change points and owners.
Glossary + Tags: Define 10 business terms (e.g., Active User, Revenue) and tag relevant datasets with these terms.

Who this is for

Data Engineers who publish datasets and maintain pipelines.
Analytics Engineers standardizing marts and models.
Data Stewards and Platform Engineers improving discoverability.

Prerequisites

Basic SQL and understanding of tables, views, and schemas.
Familiarity with your data platform’s storage and scheduling.
Ability to read pipeline DAGs or job dependencies.

Learning path

Learn dataset layers (raw/stage/mart) and naming conventions.
Define a minimal metadata template for your organization.
Document top datasets and add lineage hops.
Introduce 2–3 quality checks per critical dataset.
Expand with governance tags and glossary terms.

Next steps

Automate harvesting of technical metadata from your warehouse.
Standardize owners and on-call contacts for each domain.
Add status tags (active/deprecated) and retention policies.
Review datasets quarterly for freshness and usage.

Ready to check your understanding? Take the quick test below. The test is available to everyone; only logged-in users get saved progress.

Mini challenge

You joined a team with scattered docs. In 45 minutes, produce a one-page catalog summary for the top 5 datasets in one domain. Include owner, description, schema highlights, freshness, lineage (1 hop), and sensitivity. Timebox each dataset to 8 minutes and use a uniform template.

Quick Test

Answer a few questions to cement the concepts. The test is available to everyone; only logged-in users get saved progress.

Data Catalog And Metadata

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