Great Expectations vs OpenMetadata
Great Expectations excels in defining and enforcing data quality expectations through Python code, while OpenMetadata offers a more… See pricing, features & verdict.
Quick Comparison
| Feature | Great Expectations | OpenMetadata |
|---|---|---|
| Best For | Defining and enforcing data quality expectations in Python code | Centralized data governance with metadata management and lineage tracking |
| Architecture | Microservice-based architecture with a focus on defining data validation rules using Python | Microservices architecture with a focus on metadata collection, storage, and visualization |
| Pricing Model | Free and Open-Source, Paid upgrades available | Free and open-source under Apache 2.0 license |
| Ease of Use | Moderate to high, requires knowledge of Python and SQL for advanced use cases | Moderate to high, requires setup of metadata collectors and configuration of the platform |
| Scalability | High, can be integrated into CI/CD pipelines and scaled with cloud services like AWS Lambda | High, designed to scale horizontally across multiple clusters and data sources |
| Community/Support | Active community on GitHub, extensive documentation, and a growing number of contributors | Growing community with active development on GitHub, documentation available |
Great Expectations
- Best For:
- Defining and enforcing data quality expectations in Python code
- Architecture:
- Microservice-based architecture with a focus on defining data validation rules using Python
- Pricing Model:
- Free and Open-Source, Paid upgrades available
- Ease of Use:
- Moderate to high, requires knowledge of Python and SQL for advanced use cases
- Scalability:
- High, can be integrated into CI/CD pipelines and scaled with cloud services like AWS Lambda
- Community/Support:
- Active community on GitHub, extensive documentation, and a growing number of contributors
OpenMetadata
- Best For:
- Centralized data governance with metadata management and lineage tracking
- Architecture:
- Microservices architecture with a focus on metadata collection, storage, and visualization
- Pricing Model:
- Free and open-source under Apache 2.0 license
- Ease of Use:
- Moderate to high, requires setup of metadata collectors and configuration of the platform
- Scalability:
- High, designed to scale horizontally across multiple clusters and data sources
- Community/Support:
- Growing community with active development on GitHub, documentation available
Feature Comparison
| Feature | Great Expectations | OpenMetadata |
|---|---|---|
| Data Monitoring | ||
| Anomaly Detection | ⚠️ | ⚠️ |
| Schema Change Detection | ⚠️ | ✅ |
| Data Freshness Monitoring | ⚠️ | ⚠️ |
| Validation & Governance | ||
| Data Validation Rules | ✅ | ⚠️ |
| Data Lineage | ⚠️ | ✅ |
| Integration Breadth | ⚠️ | ⚠️ |
Data Monitoring
Anomaly Detection
Schema Change Detection
Data Freshness Monitoring
Validation & Governance
Data Validation Rules
Data Lineage
Integration Breadth
Legend:
Our Verdict
Great Expectations excels in defining and enforcing data quality expectations through Python code, while OpenMetadata offers a more comprehensive solution for centralized metadata management and data governance with features like automated profiling and lineage tracking.
When to Choose Each
Choose Great Expectations if:
When you need to define detailed data validation rules in Python code and integrate them into CI/CD pipelines.
Choose OpenMetadata if:
For organizations requiring a centralized platform for metadata management, lineage tracking, and comprehensive data governance features.
💡 This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Frequently Asked Questions
What is the main difference between Great Expectations and OpenMetadata?
Great Expectations focuses on defining and enforcing data quality expectations through Python code, whereas OpenMetadata provides a platform for centralized metadata management, lineage tracking, and comprehensive data governance.
Which is better for small teams?
For small teams focused on data validation rules in Python, Great Expectations might be more suitable. For those needing a central metadata store with lineage tracking, OpenMetadata could be the better choice.
Can I migrate from Great Expectations to OpenMetadata?
Migrating directly is not straightforward as they serve different purposes. However, you can use data quality rules defined in Great Expectations alongside OpenMetadata's governance features for a more holistic approach.
What are the pricing differences?
Both tools are open-source and do not charge licensing fees for usage. However, there may be costs associated with hosting and scaling them on cloud infrastructure.