> For the complete documentation index, see [llms.txt](https://docs.euno.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.euno.ai/using-euno/metadata-activation.md). # 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owners for descriptions * Spreadsheets to track PII and critical data ❌ **Sync Drift Between Tools** * BI models become outdated when dbt changes * Analysts rebuild logic that already exists in dbt * Inconsistent metrics across tools ❌ **Reactive Incident Response** * Discover breaking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 * 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 ✓ **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 two core capabilities: #### 1. **Workflows** 🔔 Define triggers based on your metadata and get notified when conditions are met. **How it works:** 1. Write a query (using EQL) that describes what you want to monitor 2. Set a condition (e.g., "when query returns > 0 results") 3. Choose notification channels (Slack, Email) 4. Workflows run daily and notify you when triggered **Example Use Cases:** | Scenario | Workflow | | ----------------------------- | ----------------------------------------------------------------------------------- | | **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" | [Learn more about Workflows →](#toc_24) #### 2. **Data Model Sync** 🔄 Keep your BI tools automatically in sync with your transformation logic. **How it works:** 1. Euno detects changes in your dbt manifest 2. Automatically generates updated LookML (or other BI format) 3. Creates a pull request in your BI repository 4. Your team reviews and merges the changes **Supported Platforms:** * ✓ Looker (via LookML sync from dbt) * 🔜 Tableau (coming soon) * 🔜 Power BI (coming soon) **Example Benefits:** | Without Sync | With Sync | | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- | | 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 →](/using-euno/metadata-activation/data-application-sync.md) *** ### 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:** ```none Alert #data-team when new Snowflake tables are created ``` **Intermediate Workflow:** ```none Notify owners when dbt models haven't been updated in 90 days ``` **Advanced Workflow:** ```none Alert #governance when tables containing PII are accessed by non-approved roles, and the table isn't tagged as audited ``` [Create Your First Workflow →](#toc_24) #### Step 3: Enable Data Model Sync (if applicable) If you use dbt + Looker: 1. Configure your dbt and Looker sources 2. Set up the Data Model Sync automation 3. Run your first sync to see the generated LookML 4. Review and merge the PR [Set Up Data Model Sync →](#toc_25) *** ### 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:** ```none Query: Find tables without descriptions Condition: Results > 0 Action: Send Slack message to table owner Schedule: Every Monday at 9 AM ``` **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:** 1. Analytics engineer updates dbt metric 2. Creates Jira ticket for BI team 3. BI developer recreates metric in LookML 4. Back-and-forth to verify calculation 5. 3-5 days elapsed time **After:** 1. Analytics engineer updates dbt metric 2. Euno auto-generates LookML 3. PR created automatically with exact changes 4. BI team reviews and merges 5. 15 minutes elapsed time **Results:** * 90% reduction in sync time * Zero calculation inconsistencies * BI team can focus on complex visualizations *** ### 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 *** ### 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 * 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 *** ### Next Steps 1. **Create Your First Workflow** * Start with a simple governance use case * [Workflows Guide →](#toc_24) 2. **Explore Data Model Sync** * If you use dbt + Looker * [Data Model Sync Setup →](#toc_25) 3. **Join the Community** * Share your automation ideas * Learn from other Euno users *** ### Related Documentation * [Workflows](#toc_24) - Automated notifications and alerts * [Data Model Sync](#toc_25) - Keep BI tools in sync * [Using Euno](/using-euno.md) - Day-to-day features * [Source Integrations](/sources.md) - Connect your data tools *** ### Questions? * **In-App Support:** Click the chat icon * **Email:** Ready to activate your metadata? 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