About Tamr Cloud
Tamr Cloud is an AI-native data mastering platform that creates clean, consolidated data products at scale.
What is Tamr Cloud?
Tamr Cloud is a fully managed SaaS platform that uses an AI-native approach to solve data quality problems at scale. It creates clean, consolidated, and enriched data products from your source data.
Key Capabilities
Tamr Cloud helps you:
- Master data across systems - Maintain a centralized index for data from multiple sources
- Create golden records - Build the single, authoritative version of each business entity
- Ensure data quality - Enable ongoing human review and curation
- Operationalize pipelines - Deploy data workflows at scale
- Enable real-time operations - Search, create, and update records against your System of Record
Data Products
Each Tamr Cloud data product includes:
- Industry-specific schemas (companies, healthcare providers, customers, etc.)
- Built-in data quality and enrichment services
- Pre-trained AI/ML models for deduplication
- Golden record logic for creating authoritative records
- Persistent Tamr IDs linking source records to golden records
- Human review and curation workflows
Use these data products to power analytics, BI tools, and other business applications.
Real-Time Operations
With Tamr RealTime, you can search, create, and update records directly in the Tamr System of Record (SOR). See About Tamr RealTime for details.
Why Choose Tamr Over Rules-Based Systems?
| Rules-Based Systems | Tamr Cloud |
|---|---|
Developer-created rules requiring constant updates |
Pre-built, purpose-trained ML models |
Closed systems with vendor lock-in |
Interoperability with existing tools |
High cost for adding new sources |
Easy onboarding of new data sources |
Tamr IDs
Every mastered entity gets a Tamr ID - a unique, persistent identifier that remains stable throughout the mastering process. Use these IDs to join records across datasets and maintain consistency.
Important: Maintain stable primary keys in your source data to retain persistent Tamr IDs. See Requirements for Source Datasets for details.
Updated about 2 months ago
Developer-created rules requiring constant updates
Pre-built, purpose-trained ML models
Closed systems with vendor lock-in
Interoperability with existing tools
High cost for adding new sources
Easy onboarding of new data sources