Metaplane
Data observability for the modern data stack.
Overview
Metaplane provides automated data quality monitoring for the modern data stack. It connects to data warehouses, ETL tools, and BI platforms to monitor for anomalies in freshness, volume, and schema. Its machine learning models learn data patterns to reduce alert fatigue and help teams resolve issues quickly with automated lineage maps.
✨ Key Features
- Automated Data Quality Monitoring
- ML-based Anomaly Detection
- Column-level Lineage
- Data Warehouse Cost Monitoring
- CI/CD Integration for data quality checks
🎯 Key Differentiators
- Strong focus on ease of use and fast time-to-value.
- Intelligent alert prioritization to reduce noise.
- Generous free tier makes it accessible for smaller teams.
Unique Value: Helps data teams build trust in their data by automatically detecting and resolving quality issues with minimal setup and intelligent, low-noise alerting.
🎯 Use Cases (4)
✅ Best For
- Automatically detecting and alerting on unexpected drops in row counts in critical tables.
- Tracing the root cause of a broken dashboard back to an upstream schema change using lineage.
- Monitoring Snowflake warehouse spend to prevent budget overruns.
💡 Check With Vendor
Verify these considerations match your specific requirements:
- Deep infrastructure or application performance monitoring
- Complex data pipeline performance tuning
🏆 Alternatives
Compared to more enterprise-focused platforms, Metaplane prioritizes simplicity and speed of implementation, making it a great choice for teams that need to get started with data observability quickly.
💻 Platforms
🔌 Integrations
🛟 Support Options
- ✓ Email Support
- ✓ Live Chat
- ✓ Dedicated Support (Enterprise tier)
🔒 Compliance & Security
💰 Pricing
✓ 14-day free trial
Free tier: Up to 100 models/tables, 1 data source, 2 users.
📊 Market Info
Customers: 100-200
Total Funding: $21.9M
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