Building a Data Catalog for Your Cloud Infrastructure

Source: Dev.to

Introduction

Data is the lifeblood of modern organizations, but sprawling cloud environments can make it difficult to discover, understand, and govern. A data catalog acts as a central metadata repository, providing a single source of truth about your data assets.

Why a Data Catalog Matters

Without a data catalog you’ll likely encounter:

• Data Silos – Teams operate independently, leading to duplicated efforts and inconsistent data definitions.
• Discovery Challenges – Finding the right data becomes time‑consuming and error‑prone.
• Governance Gaps – Lack of visibility hinders compliance and data‑quality initiatives.

A data catalog solves these problems by offering a searchable inventory of data assets together with their metadata (e.g., schema, lineage, ownership).

Practical Guide to Building a Data Catalog

1. Identify Data Sources & Define Objectives

Start by listing the data sources you want to include (databases, data lakes, cloud storage, etc.). Then set clear objectives:

• Discovery – Enable users to quickly find relevant datasets.
• Understanding – Provide context about data meaning, quality, and usage.
• Governance – Enforce data policies and track compliance.

2. Choose a Tooling Approach

For this example we’ll use a hybrid approach: AWS Glue Data Catalog for metadata storage and a custom Python script for automated metadata extraction.

3. Extract Metadata

3.1 Using AWS Glue Crawlers

AWS Glue Crawlers automatically scan data sources (e.g., S3 buckets, databases), infer schemas, and store the results in the Glue Data Catalog.

aws glue create-crawler \
    --name "my-s3-crawler" \
    --role "arn:aws:iam::123456789012:role/AWSGlueServiceRole" \
    --database-name "my_database" \
    --targets '{"S3Targets": [{"Path": "s3://my-data-bucket/"}]}' \
    --schedule "cron(0 12 * * ? *)"   # Run daily at 12:00 UTC

This creates a crawler named my-s3-crawler that scans s3://my-data-bucket/, infers the schema, and stores the metadata in the my_database Glue database.

3.2 Custom Extraction with Python

For sources not supported by Glue Crawlers or when you need custom metadata, you can use the boto3 library:

import boto3

glue_client = boto3.client('glue')

def extract_metadata(table_name, database_name):
    """Extracts metadata from a Glue table."""
    try:
        response = glue_client.get_table(DatabaseName=database_name, Name=table_name)
        table = response['Table']
        metadata = {
            'name': table['Name'],
            'description': table.get('Description', ''),
            'schema': table['StorageDescriptor']['Columns'],
            'location': table['StorageDescriptor']['Location'],
            'created_at': table['CreateTime'].isoformat()
        }
        return metadata
    except Exception as e:
        print(f"Error extracting metadata for {table_name}: {e}")
        return None

# Example usage
database_name = 'my_database'
table_name = 'my_table'
metadata = extract_metadata(table_name, database_name)

if metadata:
    print(metadata)

The script extracts the table name, description, schema, location, and creation time. You can extend it to pull custom tags or properties needed for governance.

4. Enrich Metadata

Enrichment adds context and improves data understanding:

• Data Lineage – Track origins and transformations (e.g., Apache Atlas, cloud‑native lineage features).
• Data Quality Metrics – Store quality check results as metadata.
• Business Glossary Integration – Link technical metadata to business terms.
• Tags & Annotations – Allow users to add custom tags.

5. Provide a User‑Friendly Interface

If you use a cloud‑native catalog, a built‑in UI is typically available. For open‑source solutions you may need to develop a custom UI.

Key UI Features

• Search – Keyword search across metadata fields.
• Filtering – Filter by source, type, tags, etc.
• Browsing – Hierarchical navigation of assets.
• Data Preview – Show sample rows (with appropriate access controls).

6. Automate Catalog Maintenance

Automation keeps the catalog current:

• Scheduled Metadata Extraction – Run crawlers or scripts on a regular cadence.
• Data Quality Monitoring – Continuously assess quality and update metadata.
• Access Control – Implement fine‑grained permissions for sensitive metadata.
• Policy Enforcement – Use the catalog to enforce governance policies.

7. Best‑Practice Recommendations

• Start Small – Begin with a subset of data sources.
• Prioritize Automation – Automate extraction and enrichment as much as possible.
• Involve Data Owners – Engage owners in the enrichment process.
• Iterate & Improve – Refine the catalog based on user feedback.

Conclusion

A well‑maintained data catalog unlocks the full potential of your data assets, strengthens governance, and accelerates data‑driven decision‑making.

Additional Tool

If you want to quickly inventory cloud assets across AWS, GCP, and Azure and identify data‑related risks, consider nuvu‑scan, a free open‑source CLI tool:

pip install nuvu-scan