Unified Cross-System Reporting
MongoDB-SQL Analytics Integration Pipeline
Integrated MongoDB and SQL systems into a centralized analytics pipeline enabling unified reporting and cross-database insights.
MongoDBSQLPythonAWS EC2Apache Superset
2+
Sources
Automated
Pipeline
Superset
Platform
Problem Statement
Business data was split across MongoDB (NoSQL) and SQL databases, making unified analytics and cross-system reporting impossible.
Architecture
Python ETL scripts on AWS EC2 extract from MongoDB and SQL, transform to unified schema, load into analytical store for Superset.
Database
MongoDB for document data, MySQL/ClickHouse for structured analytics warehouse.
Solution
Built ETL pipelines connecting MongoDB and SQL sources into a centralized analytics environment with Superset dashboards.
Key Queries
- Cross-database JOIN equivalent aggregations
- MongoDB aggregation pipeline queries
- Unified schema transformation queries
Dashboards Built
- Cross-system unified KPI dashboard
- Data source health monitoring
- Integrated business metrics panel
Business Insights
Single source of truth for business metrics
Eliminated manual data consolidation
Enabled cross-system trend analysis
Challenges
- Schema mapping between NoSQL and SQL
- Handling data sync latency
- Maintaining data consistency across sources
Future Scope
- Real-time streaming integration
- Data quality scoring
- Automated schema evolution handling
Features
Automated ETL pipelinesCross-database query layerUnified dashboard accessData sync monitoring