Architecture & Design Patterns
Proven architectural approaches for building scalable, reliable, and maintainable database systems and data infrastructure.
Reference Architecture
A simple CDC + streaming pattern: changes flow from the source database into Kafka, then fan out to operational and analytical destinations.
Common Architecture Patterns
High Availability Database Clusters
Multi-node PostgreSQL and SQL Server clusters with automatic failover, streaming replication, and load balancing for 99.99% uptime guarantees.
Key Components:
- Patroni/Pacemaker for cluster management
- Synchronous replication for zero data loss
- HAProxy/PgBouncer for connection pooling
- Distributed consensus with etcd/ZooKeeper
Event-Driven Data Architecture
Real-time data streaming using Kafka and CDC (Change Data Capture) to propagate database changes to downstream systems with sub-second latency.
Key Components:
- Debezium for database CDC
- Kafka as central event bus
- Schema Registry for contract enforcement
- Kafka Connect for sink integration
Lambda Architecture
Hybrid batch and stream processing architecture combining speed and accuracy for comprehensive analytics on large datasets.
Key Components:
- Batch layer with Apache Spark/Airflow
- Speed layer with Kafka Streams/Flink
- Serving layer with PostgreSQL/Redis
- Unified views for real-time + historical data
Medallion Architecture (Bronze/Silver/Gold)
Layered data lake architecture separating raw ingestion, cleansing/enrichment, and business-ready datasets for improved data quality and governance.
Key Components:
- Bronze: Raw data ingestion (Parquet/Avro)
- Silver: Cleaned and validated data
- Gold: Business-level aggregations
- Delta Lake/Iceberg for ACID guarantees
Polyglot Persistence
Strategic use of multiple database technologies optimized for specific workload patterns rather than one-size-fits-all approach.
Key Components:
- PostgreSQL for transactional workloads
- Redis for caching and session storage
- Elasticsearch for full-text search
- ClickHouse/TimescaleDB for time-series
Microservices Data Patterns
Database-per-service pattern with event sourcing and CQRS for maintaining consistency across distributed microservices architectures.
Key Components:
- Database per service isolation
- Event sourcing for audit trail
- CQRS for read/write separation
- Saga pattern for distributed transactions
Design Principles
Scalability First
Design systems to scale horizontally from day one, avoiding costly refactoring as data volume grows.
Defense in Depth
Multiple layers of validation, monitoring, and backup to prevent single points of failure.
Observability
Comprehensive logging, metrics, and tracing to understand system behavior and diagnose issues quickly.
Idempotency
Design data pipelines and processes to be safely retryable without side effects or duplicate data.
Data Quality as Code
Automated validation, schema enforcement, and quality checks integrated into the pipeline.
Cost Optimization
Balance performance with cost through tiered storage, compression, and efficient resource utilization.