Key Challenges in Data Lineage
Enterprise data analytics faces significant challenges when combining data lineage from one-time and complex queries. These challenges include managing diverse data sources, varying query complexity, inconsistent granularity in tracking, different real-time requirements, and cross-system integration difficulties.
AWS Services Integration
The solution leverages three powerful AWS services:
- Amazon Athena for serverless, flexible SQL analytics
- Amazon Redshift for complex queries with MPP architecture
- Amazon Neptune for efficient graph-based data lineage analysis
Unified Data Modeling with dbt
The implementation uses dbt for data modeling on both Athena and Redshift, providing several advantages:
- Consistent development language across platforms
- Reduced technical learning curve
- Automatic generation of consistent lineage information
- Enhanced adaptability to data structure changes
Architecture Components
The solution architecture incorporates:
- AWS Glue crawler for data lake information processing
- S3 buckets for storing lineage data
- Lambda functions for preprocessing and DAG generation
- Step Functions for workflow orchestration
- EventBridge for scheduled execution
Implementation Benefits
This comprehensive solution delivers multiple advantages:
- End-to-end lineage visualization
- Improved data governance capabilities
- Enhanced operational efficiency
- Cost-effective scalability
- Flexible integration options
The architecture provides a robust foundation for enterprise data lineage analysis, supporting both immediate analytical needs and complex data processing requirements while maintaining scalability and performance.
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