SOLUTION

Data Catalog Automation: AI-Powered Discovery & Documentation

Stop manually cataloging tables. Let AI build and maintain your data catalog automatically.

The Problem

Data teams spend 15-30 hours per week manually cataloging and documenting tables. Common pain points:

Manual Documentation

Engineers spend hours writing column descriptions, business logic, and relationships that get outdated within weeks.

Discovery Bottlenecks

Users can't find the data they need, leading to duplicate tables and inconsistent metrics.

Stale Metadata

Documentation falls behind actual usage, making the catalog unreliable and unused.

Expensive Tools

Traditional catalog tools require months of setup and dedicated resources to maintain.

Traditional tools require manual work. You need automation, not another system to manage.

How Our AI Agents Solve Data Catalog Automation

What used to take days:

1.Manually discover and inventory all tables
2.Interview stakeholders for business context
3.Write descriptions for each column
4.Map relationships and dependencies
5.Keep everything updated as schemas change

Now takes minutes:

1.AI scans your warehouse automatically
2.Infers business context from query patterns
3.Generates descriptions using actual data
4.Discovers relationships from usage
5.Updates continuously as you work

Time Saved:

20-30 hours/week

That's $50K-150K annually in engineering time redirected to building features instead of documentation.

Real Example

BEFORE

Manual Process

Step 1 (2 hours):

Data engineer runs queries to discover all tables in the warehouse, exports to spreadsheet

Step 2 (4 hours):

Schedule meetings with table owners to understand purpose and business logic

Step 3 (6 hours):

Write descriptions for 1,000+ tables and 10,000+ columns in catalog tool

Step 4 (3 hours):

Manually map foreign key relationships and lineage

Total: 15+ hours initial setup

Plus 5-10 hours/week ongoing maintenance

AFTER: With AI Agents

Automated Process

Step 1 (5 minutes):

Connect warehouse credentials, AI begins automatic scanning

Step 2 (Auto):

AI analyzes query logs to understand how teams actually use each table

Step 3 (Auto):

Generates descriptions using data profiling, naming conventions, and usage patterns

Step 4 (Auto):

Maps relationships automatically from JOIN patterns in queries

Total: 10 minutes initial setup

Zero ongoing maintenance - updates automatically

Result: 99% reduction in catalog maintenance time

How It Works

Automatic Discovery

AI agents connect to your warehouse and automatically discover all tables, views, and schemas. No manual inventory required.

Usage-Based Context

Agents analyze query logs to understand how your team actually uses each table. This reveals the real business purpose, common joins, and important columns.

Smart Documentation Generation

Using data profiling, naming patterns, and context from usage, AI generates human-readable descriptions for tables and columns. These are suggestions you can refine.

Relationship Mapping

AI discovers table relationships from actual JOIN patterns in queries, building an accurate lineage map without manual configuration.

Continuous Updates

As your team works and schemas evolve, the catalog updates automatically. New tables are documented, changed columns are flagged, and usage patterns keep descriptions accurate.

Why Teams Choose This Over Traditional Catalog Tools

Feature	Myriade AI Agents	Alation	Atlan
Setup Time	10 minutes	2-3 months	1-2 months
Automatic Documentation	✓	✗	✗
Usage-Based Context	✓	✓	✗
Maintenance Required	None	High	Medium
Pricing Model	Usage-based	$200K+/year	$100K+/year
AI-Powered	✓	✗	Partial

Get Started with Data Catalog Automation

See how AI can build and maintain your catalog automatically