Where raw data transforms into business intelligence. The strategic heart of Finvu’s data architecture.
🗺️ The Journey So Far
Bronze Layer
Raw data ingestion, immutable storage, complete audit trail
Silver Layer
Data transformation, cleansing, business logic application
Gold Layer
Aggregated metrics, business KPIs, ready for consumption
🎯 The Silver Layer Vision
What Silver Layer Achieves
- ✓Data Quality: Cleansing, validation, and standardization
- ✓Business Context: Applying domain knowledge and rules
- ✓Schema Evolution: Consistent, versioned data models
- ✓Performance: Optimized for analytical workloads
For Finvu’s Account Aggregation
- 🏦Unified account schemas across FIPs
- 📊Enriched transaction categorization
- 🔒Privacy-compliant data transformations
- ⚡Real-time consent state management
🏗️ Technical Architecture Deep Dive
Silver Layer Data Flow
graph TB
subgraph "Bronze Layer"
B1[Raw FIP Data]
B2[Kafka Events]
B3[API Logs]
B4[Consent Events]
end
subgraph "Silver Layer Processing"
S1[Data Quality Engine]
S2[Schema Harmonization]
S3[Business Rules Engine]
S4[Privacy Processor]
S5[Change Data Capture]
end
subgraph "Silver Tables"
ST1[accounts_silver]
ST2[transactions_silver]
ST3[consents_silver]
ST4[fip_metadata_silver]
ST5[data_quality_metrics]
end
B1 --> S1
B2 --> S2
B3 --> S3
B4 --> S4
S1 --> S5
S2 --> S5
S3 --> S5
S4 --> S5
S5 --> ST1
S5 --> ST2
S5 --> ST3
S5 --> ST4
S5 --> ST5
style S1 fill:#e1f5fe
style S2 fill:#e8f5e8
style S3 fill:#fff3e0
style S4 fill:#fce4ec
style S5 fill:#f3e5f5🔍 Data Quality Engine
- • Automated data profiling and anomaly detection
- • Schema validation against FIP specifications
- • Data completeness and accuracy scoring
- • Quarantine and remediation workflows
🔄 Schema Harmonization
- • Unified account and transaction schemas
- • FIP-specific field mapping and normalization
- • Data type standardization and conversion
- • Version management for schema evolution
⚙️ Business Rules Engine
- • Transaction categorization and enrichment
- • Account balance reconciliation logic
- • Duplicate detection and deduplication
- • Business metric calculations
🔒 Privacy Processor
- • Consent-based data access controls
- • PII masking and tokenization
- • Data retention policy enforcement
- • Audit trail for privacy compliance
🚀 Silver Layer Implementation Strategy
Foundation Phase
- • Core schema design and validation
- • Data quality framework setup
- • Basic transformation pipelines
- • Monitoring and alerting infrastructure
Enhancement Phase
- • Advanced business rules implementation
- • ML-powered data enrichment
- • Real-time processing capabilities
- • Performance optimization
Scale Phase
- • Multi-region deployment
- • Advanced analytics features
- • Self-service data access
- • Automated governance
🎯 Key Technical Decisions
Processing Architecture
Stream Processing
Real-time consent updates, account balance changes
Batch Processing
Historical data reconciliation, complex enrichments
Micro-batch
Transaction categorization, data quality checks
Storage Strategy
Delta Lake Tables
ACID transactions, time travel, schema evolution
Partitioning Strategy
By date, FIP, and account type for optimal performance
Compression & Indexing
Z-ordering, bloom filters for fast lookups
🛤️ The Path Forward