Data Warehouse Design Risks & Blind Spots

🔍 Critical Blind Spots Identified

1. CHANGE_DATA_CAPTURE_STRATEGY

WHAT_IS_CDC

CDC = Change Data Capture
DEFINITION = Process of identifying and capturing changes to data in source systems
PURPOSE = Real-time or near-real-time data synchronization
METHODS = [Database triggers, Transaction log mining, Timestamp-based, Version-based]

CDC_CHALLENGES_FOR_FINVU

SOURCE_SYSTEM = Cassandra LogDB (not CDC-friendly)
CURRENT_METHOD = Kafka streaming (good for new data)
HISTORICAL_BACKFILL = Manual export/import process
GAP = No real-time sync mechanism for Cassandra updates/corrections

CDC_SOLUTION_OPTIONS

KAFKA_AS_CDC = Treat Kafka stream as primary CDC mechanism
BATCH_RECONCILIATION = Periodic full/incremental syncs from Cassandra
TIMESTAMP_BASED = Use event_time for incremental loading
HYBRID_APPROACH = Real-time Kafka + batch reconciliation

2. DATA_QUALITY_AND_LINEAGE

SCHEMA_EVOLUTION_RISKS

JSON_PAYLOAD_CHANGES = Event structure changes without versioning
KAFKA_TOPIC_EVOLUTION = New event types without schema registry
BACKWARDS_COMPATIBILITY = Bronze layer flexibility vs Silver layer assumptions
FIELD_DEPRECATION = Legacy fields removal impact on existing queries

DATA_LINEAGE_GAPS

EVENT_CORRELATION = Complex journey stitching across multiple events
SESSIONID_MAPPING = Multiple sessions per consent handle complexity
OUTBOUND_CALL_TRACKING = txnid correlation for FIP interactions
DATA_CORRECTIONS = How to handle retroactive data fixes

3. PERFORMANCE_AND_SCALABILITY

QUERY_PERFORMANCE_UNKNOWNS

DRILL_DOWN_LATENCY = <3 second requirement across 5 levels
CONCURRENT_USERS = Multiple teams querying simultaneously
DATA_VOLUME_GROWTH = 2x market growth impact on query performance
PARTITION_STRATEGY = Monthly partitioning sufficiency at scale

CLICKHOUSE_LIMITATIONS

UPDATE_OPERATIONS = ClickHouse update limitations for data corrections
DELETE_OPERATIONS = GDPR/data deletion compliance challenges
REAL_TIME_MATERIALIZED_VIEWS = Performance impact of live aggregations
STORAGE_COSTS = Compression vs query performance trade-offs

4. BUSINESS_LOGIC_EVOLUTION

METRIC_DEFINITION_CHANGES

FUNNEL_EVOLUTION = 5-stage vs 8-stage funnel calculations
SUCCESS_RATE_DEFINITIONS = FIP vs AA attribution changes
BUSINESS_RULES = Fair Use Policy evolution impact
CLIENT_SPECIFIC_METRICS = Custom calculations per FIU requirements

REGULATORY_REPORTING_CHANGES

SAHAMATI_REQUIREMENTS = Metric definition changes by regulator
RBI_COMPLIANCE = New reporting format requirements
AUDIT_STANDARDS = Trail preservation format changes
RETENTION_POLICIES = Regulatory retention period changes

5. INTEGRATION_AND_DEPENDENCIES

EXTERNAL_SYSTEM_DEPENDENCIES

KAFKA_AVAILABILITY = Single point of failure for real-time data
CASSANDRA_MIGRATION = Legacy system dependency during transition
POSTHOG_INTEGRATION = User journey data correlation challenges
API_CHANGES = Core AA service event structure changes

TEAM_COORDINATION_RISKS

CORE_TEAM_CHANGES = Event streaming configuration changes
DEVOPS_COORDINATION = Infrastructure changes impacting data flow
BUSINESS_TEAM_REQUIREMENTS = Evolving metric needs documentation
DATA_CONSUMER_TRAINING = Multi-team adoption of new system

RISK_MITIGATION_STRATEGIES

1. CDC_AND_DATA_SYNC

KAFKA_MONITORING = Real-time monitoring of event streaming
RECONCILIATION_JOBS = Daily/weekly batch validation jobs
DATA_QUALITY_CHECKS = Automated validation against source systems
SCHEMA_REGISTRY = Event structure version management

2. PERFORMANCE_OPTIMIZATION

INCREMENTAL_MATERIALIZATION = dbt incremental models for large tables
QUERY_OPTIMIZATION = Proper indexing and partition pruning
CACHING_STRATEGY = Pre-computed aggregations for common queries
LOAD_TESTING = Performance validation under realistic load

3. BUSINESS_CONTINUITY

DUAL_SYSTEMS = Run legacy and new systems in parallel initially
ROLLBACK_STRATEGY = Quick revert to legacy system if needed
GRADUAL_MIGRATION = Team-by-team adoption approach
DOCUMENTATION = Comprehensive runbooks and troubleshooting guides

4. COMPLIANCE_AND_AUDIT

AUDIT_TRAIL = Complete lineage tracking for all transformations
DATA_GOVERNANCE = Clear ownership and approval processes
BACKUP_STRATEGY = Multiple backup retention tiers
DISASTER_RECOVERY = Multi-site data replication

MONITORING_AND_ALERTING

CRITICAL_METRICS_TO_MONITOR

DATA_FRESHNESS = Event lag from Kafka to ClickHouse
DATA_COMPLETENESS = Event count validation vs source
QUERY_PERFORMANCE = 95th percentile query response times
SYSTEM_HEALTH = ClickHouse cluster status and resource usage

BUSINESS_IMPACT_ALERTS

FUNNEL_ANOMALIES = Sudden drop in success rates
DATA_GAPS = Missing events for time periods
PERFORMANCE_DEGRADATION = Query timeouts or slow responses
RECONCILIATION_FAILURES = Source vs warehouse data mismatches

UNKNOWN_UNKNOWNS

ECOSYSTEM_EVOLUTION

SAHAMATI_CHANGES = Ecosystem protocol or standard changes
REGULATORY_SHIFTS = New compliance requirements
TECHNOLOGY_EVOLUTION = ClickHouse version upgrades, dbt changes
BUSINESS_MODEL_PIVOTS = New revenue streams requiring different analytics

SCALE_SURPRISES

VIRAL_GROWTH = Sudden 10x growth in transaction volume
NEW_CLIENT_PATTERNS = Unforeseen usage patterns from large clients
FRAUD_DETECTION = Need for real-time anomaly detection
INTERNATIONAL_EXPANSION = Multi-region data compliance requirements