Pros of DataHub

• More modern architecture with a focus on scalability and extensibility
• Richer UI and user experience, including advanced search capabilities
• Better support for cloud-native environments and microservices

Cons of DataHub

• Younger project with potentially less stability compared to Atlas
• Smaller community and ecosystem of integrations
• Steeper learning curve due to more complex architecture

Code Comparison

Atlas (Java):

public class AtlasEntity extends Referenceable {
    public static final String TYPE_NAME = "AtlasEntity";
    private Map<String, Object> attributes;
}

DataHub (Python):

class DatasetSnapshot(Snapshot):
    """Snapshot class for datasets"""
    def __init__(self, urn: str, aspects: List[Union[DatasetProperties, SchemaMetadata, ...]] = None):
        super().__init__(urn, aspects)

Both projects use different programming languages for their core implementations. Atlas primarily uses Java, while DataHub uses a combination of Python, Java, and TypeScript. The code snippets demonstrate the different approaches to defining metadata entities in each project.

Atlas follows a more traditional Java object-oriented approach, while DataHub utilizes Python's type hinting and modern language features. This reflects DataHub's more recent development and focus on developer productivity.

Pros of DataHub

• More modern architecture with a focus on scalability and extensibility
• Richer UI and user experience, including advanced search capabilities
• Better support for cloud-native environments and microservices

Cons of DataHub

• Younger project with potentially less stability compared to Atlas
• Smaller community and ecosystem of integrations
• Steeper learning curve due to more complex architecture

Code Comparison

Atlas (Java):

public class AtlasEntity extends Referenceable {
    public static final String TYPE_NAME = "AtlasEntity";
    private Map<String, Object> attributes;
}

DataHub (Python):

class DatasetSnapshot(Snapshot):
    """Snapshot class for datasets"""
    def __init__(self, urn: str, aspects: List[Union[DatasetProperties, SchemaMetadata, ...]] = None):
        super().__init__(urn, aspects)

Both projects use different programming languages for their core implementations. Atlas primarily uses Java, while DataHub uses a combination of Python, Java, and TypeScript. The code snippets demonstrate the different approaches to defining metadata entities in each project.

Atlas follows a more traditional Java object-oriented approach, while DataHub utilizes Python's type hinting and modern language features. This reflects DataHub's more recent development and focus on developer productivity.

Pros of Amundsen

• More user-friendly interface and search functionality
• Better integration with modern data stack tools (e.g., Airflow, dbt)
• Faster setup and deployment process

Cons of Amundsen

• Less comprehensive metadata management capabilities
• Smaller community and ecosystem compared to Atlas
• Limited support for complex data lineage scenarios

Code Comparison

Amundsen (Python):

class TableMetadata(BaseModel):
    database: str
    cluster: str
    schema: str
    name: str
    description: Optional[str] = None
    tags: List[str] = []

Atlas (Java):

public class AtlasEntity extends AtlasStruct implements Serializable {
    private String guid;
    private String typeName;
    private String status;
    private String createdBy;
    private String updatedBy;
}

Both projects aim to provide data discovery and metadata management solutions, but they differ in their approach and focus. Amundsen emphasizes ease of use and modern integrations, while Atlas offers more comprehensive metadata management capabilities. The code snippets showcase the different languages and data modeling approaches used by each project.

Pros of OpenLineage

• Lightweight and focused specifically on data lineage
• Easier integration with modern data stack tools
• More active community and frequent updates

Cons of OpenLineage

• Less comprehensive metadata management features
• Smaller ecosystem of integrations compared to Atlas
• Limited governance and security capabilities

Code Comparison

Atlas (Java):

AtlasEntity entity = new AtlasEntity("hive_table", "employees");
entity.setAttribute("name", "employees");
entity.setAttribute("owner", "hr_department");
atlasClient.createEntity(entity);

OpenLineage (Python):

from openlineage.client import OpenLineageClient

client = OpenLineageClient()
client.emit(
    run_id="job123",
    job_name="process_employees",
    inputs=[{"namespace": "hive", "name": "employees"}],
    outputs=[{"namespace": "hive", "name": "processed_employees"}]
)

Summary

OpenLineage is a more modern, lightweight solution focused on data lineage, while Atlas offers a more comprehensive metadata management platform. OpenLineage is easier to integrate with contemporary data tools but has fewer features for governance and security. Atlas provides a broader range of functionalities but may be more complex to set up and maintain.