Pros of DataHub

• More comprehensive data catalog solution with features like data lineage, data quality, and data governance
• Active community development with frequent updates and contributions
• Supports a wider range of data sources and integrations

Cons of DataHub

• More complex setup and configuration compared to Metacat
• Steeper learning curve due to its extensive feature set
• May require more resources to run and maintain

Code Comparison

DataHub (Python client example):

from datahub.emitter.rest_emitter import DatahubRestEmitter
from datahub.metadata.com.linkedin.pegasus2avro.metadata.snapshot import DatasetSnapshot
from datahub.metadata.schema_classes import DatasetPropertiesClass

emitter = DatahubRestEmitter("http://localhost:8080")
dataset = DatasetSnapshot(urn="urn:li:dataset:(urn:li:dataPlatform:mysql,my_database.my_table,PROD)")
dataset.aspects.append(DatasetPropertiesClass(description="My dataset description"))
emitter.emit(dataset)

Metacat (Java API example):

import com.netflix.metacat.common.server.api.v1.MetacatV1;
import com.netflix.metacat.common.dto.TableDto;

MetacatV1 api = ...;
TableDto table = api.getTable("catalog", "database", "table");
table.setMetadata(ImmutableMap.of("description", "My table description"));
api.updateTable("catalog", "database", "table", table);

Both projects aim to provide metadata management solutions, but DataHub offers a more comprehensive platform with advanced features, while Metacat focuses on simpler metadata management for data warehouses and lakes.

Pros of Amundsen

• More comprehensive data discovery and metadata management solution
• Stronger focus on data lineage and relationships between data assets
• Better suited for large-scale data ecosystems with diverse data sources

Cons of Amundsen

• More complex setup and configuration compared to Metacat
• Requires additional components (e.g., Neo4j, Elasticsearch) for full functionality
• May have a steeper learning curve for users and administrators

Code Comparison

Amundsen (Python):

from databuilder.extractor.neo4j_extractor import Neo4jExtractor
from databuilder.job.job import DefaultJob
from databuilder.loader.file_system_neo4j_csv_loader import FsNeo4jCSVLoader

job_config = ConfigFactory.from_dict({
    'extractor.neo4j.graph_url': 'bolt://localhost:7687',
    'loader.filesystem_csv_neo4j.node_dir_path': '/tmp/nodes',
    'loader.filesystem_csv_neo4j.relationship_dir_path': '/tmp/relationships',
})

Metacat (Java):

import com.netflix.metacat.common.server.properties.Config;
import com.netflix.metacat.main.api.v1.MetacatV1;

public class MetacatExample {
    private final MetacatV1 api;
    private final Config config;

    public MetacatExample(MetacatV1 api, Config config) {
        this.api = api;
        this.config = config;
    }
}

Pros of Cartography

• Focuses on security and infrastructure analysis, providing a more comprehensive view of cloud assets and their relationships
• Offers visualization capabilities, making it easier to understand complex infrastructure setups
• Supports multiple cloud providers (AWS, GCP, Azure) out of the box

Cons of Cartography

• Less emphasis on metadata management and data discovery compared to Metacat
• May require more setup and configuration for data-centric use cases
• Smaller community and fewer integrations with data processing tools

Code Comparison

Cartography (Python):

from cartography.intel.aws import ec2
from cartography.intel.aws.ec2 import sync_ec2_instances

def sync(neo4j_session, boto3_session, regions, update_tag):
    ec2.sync_ec2_instances(neo4j_session, boto3_session, regions, update_tag)

Metacat (Java):

@Slf4j
@Singleton
public class MetacatThriftHiveClient extends HiveClientFactory {
    @Inject
    public MetacatThriftHiveClient(Config config, MetacatHMSHandler handler) {
        super(config, handler);
    }
}

Pros of DataHub

• More comprehensive data catalog solution with features like data lineage, data quality, and data governance
• Active community development with frequent updates and contributions
• Supports a wider range of data sources and integrations

Cons of DataHub

• More complex setup and configuration compared to Metacat
• Steeper learning curve due to its extensive feature set
• May require more resources to run and maintain

Code Comparison

DataHub (Python client example):

from datahub.emitter.rest_emitter import DatahubRestEmitter
from datahub.metadata.com.linkedin.pegasus2avro.metadata.snapshot import DatasetSnapshot
from datahub.metadata.schema_classes import DatasetPropertiesClass

emitter = DatahubRestEmitter("http://localhost:8080")
dataset = DatasetSnapshot(urn="urn:li:dataset:(urn:li:dataPlatform:mysql,my_database.my_table,PROD)")
dataset.aspects.append(DatasetPropertiesClass(description="My dataset description"))
emitter.emit(dataset)

Metacat (Java API example):

import com.netflix.metacat.common.server.api.v1.MetacatV1;
import com.netflix.metacat.common.dto.TableDto;

MetacatV1 api = ...;
TableDto table = api.getTable("catalog", "database", "table");
table.setMetadata(ImmutableMap.of("description", "My table description"));
api.updateTable("catalog", "database", "table", table);

Both projects aim to provide metadata management solutions, but DataHub offers a more comprehensive platform with advanced features, while Metacat focuses on simpler metadata management for data warehouses and lakes.

Pros of Atlas

• More comprehensive data governance and lineage capabilities
• Stronger integration with Hadoop ecosystem components
• Active Apache project with broader community support

Cons of Atlas

• Steeper learning curve and more complex setup
• Less focus on cloud-native environments compared to Metacat
• Potentially heavier resource requirements for deployment

Code Comparison

Atlas (Java):

AtlasClient atlasClient = new AtlasClient(atlasUrls, new String[]{"admin", "admin"});
Referenceable db = new Referenceable("hive_db");
db.set("name", "default");
db.set("description", "Default Hive database");
atlasClient.createEntity(db);

Metacat (Java):

MetacatClient metacatClient = new MetacatClient(config);
DatabaseCreateRequestDto createDto = new DatabaseCreateRequestDto();
createDto.setDefinitionMetadata(ImmutableMap.of("owner", "team_data"));
metacatClient.createDatabase("hive", "default", createDto);

Both projects aim to provide metadata management solutions, but Atlas offers more extensive data governance features, while Metacat focuses on cloud-native environments and simplicity. Atlas integrates better with Hadoop ecosystems, whereas Metacat excels in multi-cloud deployments. The code snippets demonstrate the different approaches to creating database entities, with Atlas using a more detailed object model and Metacat opting for a simpler request-based approach.