⚡Metadata Activation
Overview
Metadata Activation is Euno's framework for turning passive metadata into automated actions that improve data governance, reduce manual work, and keep your data stack healthy.
Instead of just cataloging metadata, Euno lets you:
React automatically when metadata changes
Enforce policies through automated workflows
Keep systems in sync without manual intervention
Get notified about data governance issues
Why Metadata Activation Matters
Most data catalogs stop at observing your data stack. Metadata Activation takes the next step: doing something about it.
The Problem
Without Metadata Activation, your team faces:
❌ Manual Governance Enforcement
Manually checking for undocumented tables
Chasing down resource owners for descriptions
Spreadsheets to track PII and critical data
❌ Sync Drift Between Tools
BI models become outdated when dbt changes
Analysts rebuild logic that already exists in dbt
Inconsistent metrics across tools
❌ Reactive Incident Response
Discover breaking changes after they happen
Scramble to find downstream impacts
No visibility into who's affected
❌ Lost Productivity
Data teams spend time on repetitive tasks
Analysts wait for central teams to make changes
Everyone duplicates work
The Solution
Metadata Activation helps you:
✓ Automate Governance
Alert when new ungoverned resources appear
Notify owners when resources lack documentation
Track and propagate PII tags automatically
✓ Keep Tools in Sync
Auto-update BI models when dbt changes
Push metrics from dbt to Looker automatically
Ensure everyone uses the same definitions
✓ Prevent Issues Proactively
Get notified before making breaking changes
Understand blast radius of proposed changes
Alert stakeholders about upcoming impacts
✓ Scale Data Operations
Reduce manual coordination overhead
Enable self-service with guardrails
Free data teams for higher-value work
How It Works
Metadata Activation consists of three core capabilities:
1. Workflows 🔔
Define triggers based on your metadata and get notified when conditions are met.
How it works:
Write a query (using EQL) that describes what you want to monitor
Set a condition (e.g., "when query returns > 0 results")
Choose notification channels (Slack, Email)
Workflows run daily and notify you when triggered
Example Use Cases:
Governance Monitoring
"Notify #data-governance when new tables appear without a domain tag"
Documentation Enforcement
"Alert #analytics when dbt models are created without descriptions"
Usage Monitoring
"Notify dashboard owners when their dashboards haven't been viewed in 90 days"
PII Tracking
"Alert #security when tables with PII columns are queried by non-approved users"
Cost Management
"Notify #data-platform when Snowflake warehouse costs increase >20% week-over-week"
Change Detection
"Alert #bi-team when new columns are added to certified Looker explores"
2. Data Model Sync 🔄
Keep your BI tools automatically in sync with your transformation logic.
How it works:
Euno detects changes in your dbt manifest
Automatically generates updated LookML (or other BI format)
Creates a pull request in your BI repository
Your team reviews and merges the changes
Supported Platforms:
✓ Looker (via LookML sync from dbt)
🔜 Tableau (coming soon)
🔜 Power BI (coming soon)
Example Benefits:
Analytics engineer adds metric in dbt → BI developer manually recreates it in Looker → 3 days later, possibly with errors
Analytics engineer adds metric in dbt → Euno auto-generates LookML → PR created automatically → Merge in 10 minutes
Column renamed in dbt → BI dashboards break → Scramble to fix → Users see errors
Column renamed in dbt → Euno updates LookML → PR shows exactly what changed → Controlled rollout
Metrics calculated differently in dbt vs. BI → Inconsistent reports → Trust erosion
Single source of truth in dbt → Always synced to BI → Consistent definitions everywhere
Learn more about Data Model Sync →
3. Impact Analysis 📊
Understand the ripple effects of changes before you make them.
How it works:
Select a resource (table, column, dashboard, etc.)
Euno analyzes all downstream dependencies
Get a comprehensive report of what would be affected
Make informed decisions about changes
Example Questions Answered:
"If I delete this table, what dashboards will break?"
"If I change this column name, what needs to be updated?"
"If I deprecate this dbt model, what's the blast radius?"
"Who should I notify before making this change?"
Learn more about Impact Analysis →
Getting Started with Metadata Activation
Step 1: Connect Your Sources
Metadata Activation works best when Euno has visibility into your entire stack:
Connect your transformation tool (dbt)
Connect your data warehouse (Snowflake, BigQuery)
Connect your BI tools (Tableau, Looker)
More connections = more powerful automation.
Step 2: Start with Workflows
Workflows are the easiest entry point. Start with these simple use cases:
Beginner Workflow:
Intermediate Workflow:
Advanced Workflow:
Step 3: Enable Data Model Sync (if applicable)
If you use dbt + Looker:
Configure your dbt and Looker sources
Set up the Data Model Sync automation
Run your first sync to see the generated LookML
Review and merge the PR
Step 4: Use Impact Analysis Before Changes
Before deprecating a table or changing logic:
Open the resource in Euno
Run Impact Analysis
Review all downstream dependencies
Notify affected stakeholders
Proceed with confidence
Real-World Examples
Example 1: Automated Governance at Scale
Company: Mid-size B2B SaaS company Challenge: 500+ Snowflake tables, 40% undocumented Solution: Workflow that alerts table owners weekly about missing documentation
Workflow Configuration:
Results:
Documentation rate increased from 60% → 92% in 3 months
Reduced "what does this table do?" Slack questions by 75%
New tables are documented within 1 week
Example 2: dbt-to-Looker Automation
Company: E-commerce company Challenge: BI team spending 20 hours/week manually syncing dbt changes to Looker Solution: Data Model Sync from dbt to LookML
Before:
Analytics engineer updates dbt metric
Creates Jira ticket for BI team
BI developer recreates metric in LookML
Back-and-forth to verify calculation
3-5 days elapsed time
After:
Analytics engineer updates dbt metric
Euno auto-generates LookML
PR created automatically with exact changes
BI team reviews and merges
15 minutes elapsed time
Results:
90% reduction in sync time
Zero calculation inconsistencies
BI team can focus on complex visualizations
Example 3: Proactive Change Management
Company: Financial services firm Challenge: Frequent unintended dashboard breakages from upstream changes Solution: Impact Analysis before every change + automated stakeholder notifications
Process:
Developer wants to rename a column in dbt
Runs Impact Analysis in Euno
Sees 15 downstream Tableau dashboards affected
Workflow automatically notifies dashboard owners
Coordinates change during maintenance window
Results:
Zero unintended dashboard breakages in 6 months
95% reduction in "why is my dashboard broken?" incidents
Improved trust between data and analytics teams
Best Practices
For Workflows
✓ Start Small
Begin with simple, low-risk workflows
Add complexity as you learn
✓ Be Specific in Queries
Target exact resource types and conditions
Avoid overly broad notifications
✓ Choose the Right Cadence
Daily for governance checks
Immediate for critical issues
Weekly for cleanup reminders
✓ Optimize Notification Channels
Use Slack for team alerts
Use Email for individual notifications
Create dedicated channels for workflow notifications
For Data Model Sync
✓ Start with a Subset
Sync a few models first to test
Expand gradually as confidence grows
✓ Use Staging Branches
Don't sync directly to production
Review PRs before merging
✓ Document Naming Conventions
Handle collisions consistently
Use meta keys for custom naming
✓ Monitor Sync Health
Review sync logs regularly
Address failures quickly
For Impact Analysis
✓ Run Before Every Breaking Change
Column renames
Table drops
Schema changes
✓ Document Your Findings
Export impact reports
Share with stakeholders
Track in project management tools
✓ Communicate Early
Notify affected teams before changes
Provide timeline and migration plan
Offer support during transition
Success Metrics
How to measure the impact of Metadata Activation:
Governance Metrics
% of resources with documentation
% of tables with appropriate tags
Time to document new resources
Efficiency Metrics
Time spent on manual sync tasks
Number of governance-related Slack messages
Hours saved per week on repetitive work
Quality Metrics
Number of dashboard breaking incidents
Metric consistency across tools
Data trust survey scores
Adoption Metrics
Number of active workflows
% of changes using Impact Analysis
Team satisfaction scores
Limitations & Considerations
Workflows
Execute maximum once per day
60-second query timeout
Maximum 100,000 results per query
Requires source integrations to be active
Data Model Sync
Currently supports dbt → Looker only
Requires Git access for PR creation
Some complex dbt patterns may not sync
Requires review before production deployment
Impact Analysis
Depends on connected sources
Limited to relationships Euno can observe
May not capture external dependencies
Requires up-to-date metadata
Next Steps
Create Your First Workflow
Start with a simple governance use case
Explore Data Model Sync
If you use dbt + Looker
Try Impact Analysis
Before your next schema change
Join the Community
Share your automation ideas
Learn from other Euno users
Related Documentation
Workflows - Automated notifications and alerts
Data Model Sync - Keep BI tools in sync
Impact Analysis - Understand change impact
Using Euno - Day-to-day features
Source Integrations - Connect your data tools
Questions?
In-App Support: Click the chat icon
Email: [email protected]
Documentation: Full Metadata Activation Docs
Ready to activate your metadata? Start with Workflows →
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