Skip to main content

High-Level Architecture

Dataspine is built as a cloud-native, event-driven platform that provides real-time data processing capabilities with strong consistency guarantees. This document describes the overall architecture and how the various components work together.

Architecture Overview​

Core Components​

1. Ingestion Layer​

The ingestion layer is responsible for capturing data from external systems in real-time.

Change Data Capture (CDC)​

  • Purpose: Capture database changes in real-time
  • Supported Databases: PostgreSQL, MySQL, MongoDB, SQL Server
  • Technology: Debezium-based CDC with custom optimizations
  • Guarantees: Exactly-once delivery, ordered processing

Connectors​

  • Purpose: Integrate with various data sources and systems
  • Types: Database, API, File, Message Queue, Custom
  • Features: Schema inference, error handling, backpressure management

Schema Registry​

  • Purpose: Manage schema evolution and compatibility
  • Features: Backward/forward compatibility, schema validation, versioning
  • Storage: Distributed schema store with ACID properties

2. Processing Layer​

The heart of Dataspine's real-time processing capabilities.

Stream Processor​

  • Technology: Custom Rust-based stream processing engine
  • Features:
    • Sub-millisecond latency
    • Exactly-once semantics
    • Automatic parallelization
    • Fault tolerance and recovery

State Store​

  • Purpose: Manage stateful transformations and aggregations
  • Technology: RocksDB with custom optimizations
  • Features:
    • ACID transactions
    • Point-in-time recovery
    • Distributed state management

Transformations​

  • Languages: Rust (native), SQL, Python
  • Patterns: Map, Filter, Join, Aggregate, Window operations
  • Optimization: Automatic query optimization and vectorization

3. Storage Layer​

Durable storage for events, metadata, and operational data.

Event Store​

  • Purpose: Immutable log of all events and transformations
  • Technology: Apache Kafka with custom retention policies
  • Features:
    • Infinite retention options
    • Time-travel queries
    • Event replay capabilities

Metadata Store​

  • Purpose: Store configuration, schemas, and operational metadata
  • Technology: PostgreSQL with high availability setup
  • Data: Data product definitions, lineage, access policies

Cache Layer​

  • Purpose: High-performance caching for frequently accessed data
  • Technology: Redis Cluster with intelligent cache warming
  • Features: Automatic invalidation, distributed caching

4. API Layer​

Unified access to data products through multiple protocols.

Auto-Generated APIs​

Every data product automatically exposes:

# API endpoints automatically created
/api/v1/{data-product}/           # REST API
/graphql/{data-product}/          # GraphQL endpoint
/grpc/{data-product}/             # gRPC service
/stream/{data-product}/           # WebSocket streaming

Protocol Support​

  • REST: Full CRUD operations with pagination and filtering
  • GraphQL: Flexible queries with real-time subscriptions
  • gRPC: High-performance binary protocol for service-to-service
  • WebSockets: Real-time streaming to web applications

5. Control Plane​

Management and operational capabilities.

Deployment Engine​

  • Features: Blue-green deployments, canary releases, rollback
  • Integration: Kubernetes-native with Helm charts
  • Automation: GitOps workflows, CI/CD integration

Monitoring & Observability​

  • Metrics: Prometheus-compatible metrics collection
  • Tracing: Distributed tracing with OpenTelemetry
  • Logging: Structured logging with correlation IDs
  • Dashboards: Grafana-based operational dashboards

Governance​

  • Access Control: Fine-grained RBAC with OpenFGA
  • Data Classification: Automatic PII detection and tagging
  • Audit: Complete audit trails for all operations
  • Compliance: GDPR, CCPA, and other regulatory compliance

Data Flow Architecture​

Real-time Data Flow​

Batch Integration Flow​

Scalability and Performance​

Horizontal Scaling​

Stream Processing​

  • Auto-scaling: Based on data volume and latency requirements
  • Partitioning: Automatic data partitioning for parallel processing
  • Load Balancing: Intelligent load distribution across processors

Storage Scaling​

  • Event Store: Automatic partition rebalancing
  • State Store: Distributed state management with consistent hashing
  • Cache Layer: Elastic cache scaling based on hit rates

Performance Characteristics​

ComponentLatencyThroughputAvailability
CDC Ingestion< 10ms1M events/sec99.99%
Stream Processing< 1ms10M events/sec99.99%
API Queries< 5ms100K req/sec99.95%
Event Store< 1ms1M writes/sec99.99%

Security Architecture​

Defense in Depth​

Security Features​

Authentication & Authorization​

  • SSO Integration: SAML, OIDC, Active Directory
  • Fine-grained RBAC: Resource and action-level permissions
  • API Keys: Service-to-service authentication
  • Zero Trust: All communications verified and encrypted

Data Protection​

  • Encryption: AES-256 at rest, TLS 1.3 in transit
  • Key Management: Hardware Security Modules (HSM)
  • Data Masking: Dynamic masking based on user roles
  • Pseudonymization: Consistent tokenization for analytics

Deployment Architecture​

Multi-Region Deployment​

Infrastructure Components​

Kubernetes Native​

  • Orchestration: Kubernetes 1.24+
  • Service Mesh: Istio for traffic management
  • Ingress: NGINX with automatic SSL/TLS
  • Storage: Persistent volumes with automatic backup

Cloud Provider Integration​

  • AWS: EKS, RDS, ElastiCache, S3
  • Azure: AKS, PostgreSQL, Redis, Blob Storage
  • GCP: GKE, Cloud SQL, Memorystore, Cloud Storage

High Availability & Disaster Recovery​

Availability Guarantees​

Service Level Objectives (SLOs)​

  • Platform Availability: 99.99% uptime
  • API Latency: P99 < 100ms
  • Data Freshness: < 1 second for real-time data
  • Recovery Time: < 5 minutes for any component failure

Fault Tolerance​

  • Automatic Failover: Immediate failover to healthy instances
  • Circuit Breakers: Prevent cascade failures
  • Graceful Degradation: Continue operating with reduced functionality
  • Self-Healing: Automatic recovery from transient failures

Disaster Recovery​

Backup Strategy​

  • Continuous Replication: Real-time data replication to secondary regions
  • Point-in-Time Recovery: Restore to any point in the last 30 days
  • Configuration Backup: All configurations stored in version control
  • Cross-Region Recovery: Automatic failover to backup regions

Next Steps​

To dive deeper into specific aspects of the architecture:

Deployment, security, and low-level platform internals are covered by internal documentation where applicable.