Back to the top

Platform Infrastructure & Supporting Services

The infrastructure layer provides the foundational services, networking, security, and operational capabilities that underpin the Gateway, Registry, Auditor, and Developer Portal. This section covers the shared infrastructure components, cross-cutting services, and operational tooling required to run the API governance platform at scale.

5.1 Container Orchestration & Compute

Kubernetes Cluster Architecture

Purpose: Provides the container orchestration platform for deploying and managing all API governance components.

Key Responsibilities:

• Multi-cluster deployment
  • Production cluster for Gateway, Registry, Auditor, Portal
  • Non-production clusters for dev, test, staging environments
  • Regional clusters for geographic distribution and disaster recovery
  • Dedicated clusters for PCI/HIPAA workloads (compliance isolation)
• Workload management
  • Deployment manifests for all platform components
  • StatefulSets for databases and stateful services
  • DaemonSets for logging agents, monitoring exporters
  • CronJobs for scheduled tasks (cleanup, reporting)
• Auto-scaling
  • Horizontal Pod Autoscaler (HPA) based on CPU, memory, custom metrics
  • Vertical Pod Autoscaler (VPA) for right-sizing resource requests
  • Cluster autoscaler for node pool scaling
  • Event-driven autoscaling (KEDA) for queue-based workloads
• Resource management
  • Namespace isolation per component (gateway, registry, auditor, portal)
  • Resource quotas and limit ranges
  • Pod priority classes for critical workloads
  • Pod disruption budgets for availability during updates
• Service networking
  • ClusterIP services for internal communication
  • LoadBalancer services for external access
  • Ingress controllers (NGINX, Traefik, AWS ALB) for HTTP routing
  • NetworkPolicies for microsegmentation

Technical Implementation:

• Managed Kubernetes: EKS, GKE, AKS for cloud-native deployment
• Self-managed: kubeadm, Rancher for on-premises or hybrid
• GitOps: ArgoCD or FluxCD for declarative deployments
• Helm charts for packaging and versioning applications

Inputs:

• Application container images from artifact registry
• Deployment manifests from Git repositories
• Configuration from ConfigMaps and Secrets
• Scaling policies and resource requirements

Outputs:

• Running pods and services across cluster
• Cluster metrics (node health, pod status, resource utilization)
• Audit logs of cluster operations
• Cluster events for monitoring

---

Service Mesh Infrastructure

Purpose: Provides advanced networking, security, and observability for microservices communication.

Key Responsibilities:

• Traffic management
  • Intelligent load balancing (round-robin, least-request, consistent hashing)
  • Circuit breaking and outlier detection
  • Retry logic and timeout policies
  • Traffic splitting for canary deployments and A/B testing
  • Request mirroring for shadow traffic
• Security
  • Mutual TLS (mTLS) for all service-to-service communication
  • Certificate management and automatic rotation
  • Service-level authorization policies
  • Encryption in transit by default
• Observability
  • Distributed tracing with automatic span generation
  • Service-level metrics (request rate, latency, error rate)
  • Service dependency graph visualization
  • Access logging for all requests
• Resilience
  • Fault injection for chaos engineering
  • Rate limiting at service level
  • Connection pooling and keepalive management
  • Health checking with active/passive probes

Technical Implementation:

• Service mesh: Istio, Linkerd, or AWS App Mesh
• Sidecar proxies: Envoy injected into each pod
• Control plane: istiod for configuration distribution
• Observability integration: Jaeger, Kiali, Prometheus

Inputs:

• Service mesh configuration (VirtualServices, DestinationRules)
• mTLS certificates from cert-manager or service mesh CA
• Traffic policies and security rules

Outputs:

• Encrypted service-to-service traffic
• Distributed traces and service metrics
• Service mesh telemetry for monitoring
• Traffic management enforcement

---

5.2 Load Balancing & Traffic Management

Global Load Balancing & DNS

Purpose: Distributes traffic across regions and provides DNS-based routing for high availability.

Key Responsibilities:

• Global traffic distribution
  • Geographic routing (route users to nearest region)
  • Latency-based routing (route to fastest endpoint)
  • Failover routing (redirect traffic from unhealthy regions)
  • Weighted routing for gradual region migrations
• DNS management
  • Authoritative DNS for api.company.com
  • Health checks on regional endpoints
  • TTL management for fast failover
  • DNSSEC for security
• DDoS protection
  • Rate limiting at edge
  • IP reputation filtering
  • Geographic blocking for untrusted regions
  • Cloudflare, AWS Shield, or Akamai integration
• Web Application Firewall (WAF)
  • OWASP Top 10 protection
  • SQL injection and XSS prevention
  • Bot detection and mitigation
  • Custom rules for API-specific threats

Technical Implementation:

• DNS: Route53, Cloudflare, or Google Cloud DNS
• Global load balancing: AWS Global Accelerator, Cloudflare, or Akamai
• WAF: AWS WAF, Cloudflare WAF, or ModSecurity
• DDoS protection: AWS Shield Advanced, Cloudflare, Imperva

Inputs:

• Health check endpoints from Gateway and Portal
• Traffic routing policies
• WAF rules and IP blocklists
• DNS records and zone configurations

Outputs:

• Distributed traffic across healthy regions
• DNS query responses with optimal routing
• Blocked malicious traffic
• Traffic and security metrics

---

Application Load Balancers

Purpose: Provides Layer 7 load balancing for HTTP/HTTPS traffic to platform components.

Key Responsibilities:

• Load balancing algorithms
  • Round-robin for even distribution
  • Least connections for long-lived connections
  • Sticky sessions (if needed for stateful apps)
  • IP hashing for consistent routing
• TLS termination
  • Certificate management (ACM, Let’s Encrypt)
  • SNI support for multi-domain hosting
  • TLS 1.2 minimum, TLS 1.3 preferred
  • Cipher suite configuration
• Health checking
  • HTTP health checks with custom endpoints
  • Configurable intervals and thresholds
  • Automatic removal of unhealthy targets
  • Graceful draining during deployments
• Path-based routing
  • Route /api/* to Gateway
  • Route /portal/* to Developer Portal
  • Route /registry/* to Registry API
  • Route /metrics/* to monitoring endpoints
• Request routing features
  • Header-based routing (X-Version, X-Environment)
  • Host-based routing (api.company.com vs. portal.company.com)
  • Request rewriting and redirects
  • CORS configuration

Technical Implementation:

• Cloud load balancers: AWS ALB/NLB, GCP Load Balancer, Azure Load Balancer
• Kubernetes Ingress: NGINX Ingress Controller, Traefik, Ambassador
• Proxy: HAProxy, Envoy for on-premises
• Certificate management: cert-manager for Kubernetes, ACM for AWS

---

5.3 Data Storage Infrastructure

Relational Databases

Purpose: Provides managed relational database services for Registry and platform metadata.

Key Responsibilities:

• Primary databases
  • Registry database (API metadata, subscriptions, applications)
  • User database (portal users, teams, permissions)
  • Platform configuration database
• High availability
  • Multi-AZ deployment with automatic failover
  • Synchronous replication for data durability
  • Read replicas for query scaling
  • Automated backups with point-in-time recovery
• Performance optimization
  • Connection pooling (PgBouncer, ProxySQL)
  • Query performance monitoring
  • Index optimization
  • Partition management for large tables
• Security
  • Encryption at rest (AES-256)
  • Encryption in transit (TLS)
  • IAM database authentication
  • Network isolation (VPC, private subnets)
  • Automated security patching

Technical Implementation:

• Managed services: AWS RDS/Aurora, Google Cloud SQL, Azure Database
• Database engine: PostgreSQL for relational data + JSONB for flexibility
• Backup: Automated daily backups, 35-day retention, cross-region replication
• Monitoring: CloudWatch, Cloud SQL Insights, or Datadog

Inputs:

• Application connections from Registry, Portal, Auditor
• SQL queries for reads and writes
• Backup schedules and retention policies

Outputs:

• Database query results
• Backup snapshots
• Performance metrics (connections, queries/sec, latency)
• Replication lag metrics

---

Time-Series Databases

Purpose: Stores metrics and telemetry data for monitoring and analytics.

Key Responsibilities:

• Metrics storage
  • Gateway metrics (request rate, latency, errors)
  • Infrastructure metrics (CPU, memory, disk, network)
  • Application metrics (custom business metrics)
  • Auditor metrics (log ingestion rate, query performance)
• Data retention
  • High-resolution data (1-minute) for 7 days
  • Medium-resolution (1-hour) for 90 days
  • Low-resolution (1-day) for 2 years
  • Automated downsampling and retention policies
• Query performance
  • Optimized for time-range queries
  • Aggregation functions (sum, avg, percentiles)
  • Multi-dimensional queries (group by labels)
  • Indexing on tags and labels

Technical Implementation:

• Time-series DB: Prometheus, InfluxDB, TimescaleDB, or AWS Timestream
• Storage: Local SSD for hot data, S3 for cold data (Thanos for Prometheus)
• Retention: Automated compaction and downsampling
• Federation: Prometheus federation for multi-cluster metrics

---

Object Storage

Purpose: Provides scalable, durable storage for logs, artifacts, and large files.

Key Responsibilities:

• Log archival
  • Gateway access logs (long-term compliance storage)
  • Audit logs (immutable, tamper-proof storage)
  • Application logs from all platform components
• Artifact storage
  • API specifications (OpenAPI, GraphQL schemas)
  • Container images (Docker registry backend)
  • Deployment artifacts (Helm charts, Terraform modules)
  • Backup files (database dumps, configuration backups)
• Static assets
  • Developer Portal static files (CSS, JS, images)
  • Documentation assets (diagrams, screenshots, videos)
  • SDK and code sample downloads
• Lifecycle management
  • Transition to cheaper storage tiers after 30/90 days
  • Automatic expiration of temporary files
  • Versioning for critical artifacts
  • Object lock for compliance (WORM storage)

Technical Implementation:

• Cloud object storage: AWS S3, Google Cloud Storage, Azure Blob Storage
• Lifecycle policies: Automated transitions (S3 Standard → Glacier)
• Versioning: Enabled for audit logs and specifications
• Replication: Cross-region replication for disaster recovery

---

Distributed Caching

Purpose: Provides high-performance caching layer to reduce database load and improve latency.

Key Responsibilities:

• Cache use cases
  • Registry API responses (API metadata, subscriptions)
  • Gateway configuration (routing rules, rate limits)
  • Authentication tokens (validated tokens cached for TTL)
  • Session data (user sessions in Developer Portal)
  • Frequently accessed analytics data
• Cache patterns
  • Read-through cache (cache miss fetches from DB)
  • Write-through cache (writes update cache and DB)
  • Cache-aside (application manages cache population)
  • TTL-based expiration
  • Explicit invalidation on data changes
• High availability
  • Redis cluster with automatic failover
  • Multi-AZ deployment
  • Persistence (RDB snapshots, AOF logs)
  • Backup and restore capabilities
• Performance
  • Sub-millisecond latency for cached data
  • Connection pooling from applications
  • Monitoring cache hit/miss rates
  • Eviction policies (LRU, LFU)

Technical Implementation:

• In-memory cache: Redis, Memcached, or AWS ElastiCache
• Clustering: Redis Cluster or Redis Sentinel for HA
• Client libraries: Jedis, ioredis, redis-py with connection pooling
• Monitoring: Cache hit rate, eviction rate, memory usage

---

Search Infrastructure

Purpose: Provides full-text search capabilities for API catalog and documentation.

Key Responsibilities:

• Search indexing
  • API catalog (names, descriptions, tags, endpoints)
  • Documentation content (guides, tutorials, forum posts)
  • Error messages and troubleshooting guides
  • Log searching for debugging
• Search features
  • Full-text search with relevance scoring
  • Autocomplete and suggestions
  • Faceted filtering (category, status, owner)
  • Fuzzy matching and spell correction
  • Highlighting search results
• Performance
  • Sub-second search response times
  • Horizontal scaling for query load
  • Index sharding and replication
  • Query caching for common searches

Technical Implementation:

• Search engine: Elasticsearch, OpenSearch, or Algolia
• Indexing pipeline: Logstash, custom indexers syncing from Registry
• Cluster: Multi-node cluster with primary and replica shards
• Monitoring: Query latency, index size, cluster health

---

5.4 Message Queues & Event Streaming

Event Streaming Platform

Purpose: Provides real-time event streaming for log ingestion, metrics, and platform events.

Key Responsibilities:

• Log ingestion
  • Stream Gateway access logs in real-time
  • Ingest application logs from all components
  • High-throughput ingestion (millions of events/sec)
  • Buffering to handle traffic spikes
• Event-driven architecture
  • API lifecycle events (published, deprecated, retired)
  • Subscription events (created, approved, revoked)
  • Alert events (SLA violations, security incidents)
  • Workflow events (approval state changes)
• Stream processing
  • Real-time aggregation (request counts, error rates)
  • Filtering and routing (route events to different consumers)
  • Enrichment (join with metadata from Registry)
  • Windowing and time-based aggregations
• Consumers
  • Auditor ingestion pipeline (logs → Elasticsearch)
  • Metrics aggregation (events → Prometheus)
  • Notification service (events → emails/Slack)
  • Data warehouse ETL (events → BigQuery/Snowflake)

Technical Implementation:

• Streaming platform: Apache Kafka, AWS Kinesis, Google Pub/Sub, Azure Event Hubs
• Topic design: Separate topics per event type (logs, metrics, lifecycle-events)
• Retention: 7-day retention for log replay
• Partitioning: Partition by subscription ID for parallelism
• Consumer groups: Multiple consumers for scalability

---

Asynchronous Job Queues

Purpose: Handles background jobs and asynchronous processing for platform operations.

Key Responsibilities:

• Job types
  • Subscription approval workflows (multi-step approval process)
  • Report generation (scheduled analytics reports)
  • Bulk operations (batch API updates, data migrations)
  • Notification delivery (email, Slack, webhooks)
  • Data synchronization (Registry → Gateway config sync)
• Job scheduling
  • Immediate execution (FIFO queue)
  • Delayed execution (schedule for later)
  • Recurring jobs (cron-style scheduling)
  • Priority queues (critical jobs first)
• Reliability
  • Job retry with exponential backoff
  • Dead-letter queue for failed jobs
  • At-least-once delivery guarantees
  • Idempotent job processing
• Monitoring
  • Queue depth and lag
  • Job success/failure rates
  • Processing time per job type
  • Worker health and capacity

Technical Implementation:

• Message queue: RabbitMQ, AWS SQS, Google Cloud Tasks, Azure Queue Storage
• Worker framework: Celery (Python), Sidekiq (Ruby), Bull (Node.js)
• Scheduler: Cron, Airflow, or cloud-native schedulers
• Persistence: Queue backed by durable storage

---

5.5 Security & Secrets Management

Secrets Management Service

Purpose: Securely stores and manages sensitive credentials, API keys, and encryption keys.

Key Responsibilities:

• Secret storage
  • Database credentials for Registry, Portal
  • API keys for external services (email, monitoring, cloud APIs)
  • TLS certificates and private keys
  • Encryption keys for data-at-rest encryption
  • OAuth client secrets
• Secret distribution
  • Inject secrets into containers as environment variables or files
  • Dynamic secret generation (ephemeral database credentials)
  • Secret rotation without application restarts
  • Version tracking of secret changes
• Access control
  • Fine-grained policies (which service can access which secrets)
  • Audit logging of secret access
  • MFA for human access to secrets
  • Lease management and TTL for temporary secrets
• Encryption key management
  • Master encryption keys for database encryption
  • Data encryption keys (DEKs) wrapped by master keys
  • Key rotation schedules
  • FIPS 140-2 compliant HSMs for high-security keys

Technical Implementation:

• Secrets manager: HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, Google Secret Manager
• Kubernetes integration: External Secrets Operator, Vault Agent injector
• Encryption: KMS for key management (AWS KMS, Google Cloud KMS)
• Rotation: Automated rotation for RDS, API keys

---

Certificate Management

Purpose: Manages TLS certificates for HTTPS endpoints and mTLS service communication.

Key Responsibilities:

• Certificate issuance
  • Automated certificate requests from Let’s Encrypt or internal CA
  • Wildcard certificates for *.api.company.com
  • SAN certificates for multiple domains
  • Client certificates for mTLS authentication
• Certificate lifecycle
  • Automated renewal before expiration (30 days)
  • Certificate revocation and replacement
  • Certificate rotation with zero downtime
  • Expiration monitoring and alerting
• Certificate distribution
  • Inject certificates into load balancers
  • Distribute to Gateway instances for TLS termination
  • Provide to service mesh for mTLS
  • Store in Kubernetes secrets for ingress controllers

Technical Implementation:

• Certificate authority: Let’s Encrypt (public), internal PKI (private)
• Kubernetes: cert-manager for automated certificate management
• Cloud services: AWS ACM, Google-managed certificates
• Monitoring: Certificate expiration alerts (30, 15, 7 days)

---

Identity & Access Management (IAM)

Purpose: Manages authentication and authorization for platform services and users.

Key Responsibilities:

• Service accounts
  • IAM roles for Gateway, Registry, Auditor, Portal
  • Principle of least privilege (minimal permissions)
  • Service-to-service authentication
  • Workload identity (GKE, EKS IAM roles for service accounts)
• User authentication
  • SSO integration (SAML, OIDC) for employees
  • Social login (GitHub, Google) for external developers
  • Multi-factor authentication (MFA) for privileged access
  • Session management and token refresh
• Authorization
  • Role-based access control (RBAC) for platform operations
  • Attribute-based access control (ABAC) for fine-grained permissions
  • Policy-as-code (OPA, Cedar) for authorization logic
  • Permission inheritance (team-level, org-level)
• Audit logging
  • Log all authentication attempts (success and failure)
  • Log authorization decisions
  • User activity tracking
  • Integration with SIEM for security monitoring

Technical Implementation:

• Cloud IAM: AWS IAM, Google Cloud IAM, Azure AD
• Identity provider: Okta, Auth0, Keycloak for user authentication
• Authorization: Open Policy Agent, AWS IAM policies, custom RBAC
• Audit: CloudTrail, Cloud Audit Logs for IAM events

---

5.6 Observability & Monitoring Stack

Centralized Logging

Purpose: Aggregates logs from all platform components for troubleshooting and compliance.

Key Responsibilities:

• Log collection
  • Application logs from Gateway, Registry, Auditor, Portal
  • System logs from Kubernetes nodes
  • Audit logs from infrastructure (IAM, database)
  • Security logs (authentication failures, access attempts)
• Log processing
  • Parsing and structuring (JSON, syslog)
  • Filtering and sampling (reduce volume)
  • Enrichment (add metadata, geographic info)
  • Redaction (remove PII from logs)
• Log storage
  • Hot storage (last 7 days) for fast searching
  • Warm storage (30 days) for troubleshooting
  • Cold storage (1 year+) for compliance
  • Archival to object storage
• Log querying
  • Full-text search across all logs
  • Filtering by service, timestamp, severity
  • Correlation with trace IDs
  • Log visualization and dashboards

Technical Implementation:

• Log aggregation: Fluentd, Logstash, Fluent Bit as log shippers
• Log storage: Elasticsearch, Splunk, CloudWatch Logs, Datadog
• Log router: Vector for high-performance log routing
• Visualization: Kibana, Grafana Loki for log exploration

---

Metrics & Monitoring

Purpose: Collects and visualizes metrics for platform health and performance monitoring.

Key Responsibilities:

• Metric collection
  • Infrastructure metrics (CPU, memory, disk, network)
  • Application metrics (request rate, latency, errors)
  • Business metrics (API subscriptions, revenue)
  • Custom metrics from platform components
• Metric aggregation
  • Time-series data at multiple resolutions
  • Pre-aggregated rollups for dashboards
  • Multi-dimensional metrics (tags, labels)
  • Statistical aggregations (percentiles, rates)
• Alerting
  • Threshold-based alerts (CPU > 80%, error rate > 1%)
  • Anomaly detection alerts (traffic spike, latency increase)
  • Composite alerts (multiple conditions)
  • Alert routing (email, Slack, PagerDuty)
  • Alert suppression during maintenance
• Dashboards
  • Platform overview (all components health)
  • Per-component dashboards (Gateway, Registry, Auditor)
  • SLA dashboards (availability, latency, errors)
  • Business dashboards (API growth, adoption)

Technical Implementation:

• Metrics collection: Prometheus, CloudWatch, Datadog agents
• Time-series storage: Prometheus, InfluxDB, Timestream
• Visualization: Grafana, Kibana, Datadog dashboards
• Alerting: Prometheus Alertmanager, CloudWatch Alarms, Datadog monitors

---

Distributed Tracing

Purpose: Provides end-to-end visibility into request flows across microservices.

Key Responsibilities:

• Trace collection
  • Trace spans from Gateway (API request entry)
  • Spans from Registry (metadata lookups)
  • Spans from Auditor (log processing)
  • Spans from backend services
• Trace correlation
  • Propagate trace context (trace ID, span ID, parent span ID)
  • Link logs to traces via trace ID
  • Correlate errors with traces
  • Service dependency mapping
• Trace analysis
  • Latency breakdown per service
  • Critical path identification
  • Error tracing to root cause
  • Performance bottleneck detection
• Trace visualization
  • Waterfall diagrams showing span timeline
  • Service dependency graphs
  • Trace search by filters (latency, errors, service)
  • Trace comparison (compare slow vs. fast traces)

Technical Implementation:

• Tracing protocol: OpenTelemetry (standard)
• Tracing backend: Jaeger, Zipkin, AWS X-Ray, Datadog APM
• Instrumentation: OpenTelemetry SDKs in applications, service mesh automatic tracing
• Sampling: Probabilistic sampling (trace 1% of requests), always trace errors

---

Alerting & Incident Management

Purpose: Detects issues and routes alerts to appropriate teams for rapid response.

Key Responsibilities:

• Alert generation
  • Metric-based alerts (threshold violations, anomalies)
  • Log-based alerts (error patterns, security events)
  • Synthetic monitoring alerts (API endpoint down)
  • Infrastructure alerts (node failure, disk full)
• Alert routing
  • Route to on-call engineer via PagerDuty/Opsgenie
  • Escalation policies (if not acknowledged in 15 min, escalate)
  • Team-specific routing (Gateway alerts → Gateway team)
  • Multi-channel delivery (push, SMS, phone call, Slack)
• Incident management
  • Create incidents from critical alerts
  • Incident timeline and status tracking
  • Runbooks and remediation guides
  • Post-incident reviews and RCA
• Alert fatigue reduction
  • Alert grouping and deduplication
  • Intelligent alert suppression
  • Noise reduction (filter transient alerts)
  • Alert tuning based on feedback

Technical Implementation:

• Alerting platform: PagerDuty, Opsgenie, VictorOps
• Incident management: ServiceNow, Jira Service Management, Incident.io
• Runbooks: PagerDuty Runbook Automation, custom playbooks
• Integration: Bi-directional sync with monitoring tools

---

5.7 Development & Operations Tooling

CI/CD Pipelines

Purpose: Automates building, testing, and deploying platform components.

Key Responsibilities:

• Build pipelines
  • Container image builds from source code
  • Multi-stage Docker builds for optimization
  • Security scanning (container images, dependencies)
  • Artifact publishing to registry
• Test automation
  • Unit tests, integration tests, e2e tests
  • API contract testing (ensure Gateway compatible with specs)
  • Performance testing (load tests, stress tests)
  • Security testing (SAST, DAST, dependency scanning)
• Deployment automation
  • Kubernetes manifest deployment via Helm or Kustomize
  • Blue-green deployments for zero downtime
  • Canary deployments with automated rollback
  • Database migrations (automated, rollback-safe)
• Release management
  • Semantic versioning enforcement
  • Changelog generation
  • Release notes automation
  • Environment promotion (dev → test → staging → prod)

Technical Implementation:

• CI/CD platform: GitHub Actions, GitLab CI, Jenkins, CircleCI, AWS CodePipeline
• Container registry: Docker Hub, AWS ECR, Google Artifact Registry, Harbor
• Deployment: ArgoCD, FluxCD for GitOps, Spinnaker for advanced deployments
• Testing: Jest, Pytest, Postman/Newman for API tests

---

Infrastructure as Code (IaC)

Purpose: Manages infrastructure declaratively with version control and automation.

Key Responsibilities:

• Infrastructure provisioning
  • Kubernetes clusters (EKS, GKE, AKS)
  • Databases (RDS, Cloud SQL)
  • Networking (VPCs, subnets, security groups)
  • Load balancers, DNS, CDN
  • Monitoring and logging infrastructure
• Configuration management
  • Kubernetes manifests (deployments, services, ingress)
  • Helm charts for application packaging
  • ConfigMaps and Secrets
  • Service mesh configuration
• Environment parity
  • Shared modules for dev, test, prod
  • Environment-specific variables
  • Consistent infrastructure across regions
  • Automated environment creation
• Change management
  • Code review for infrastructure changes
  • Plan/preview before apply
  • Automated testing of infrastructure changes
  • Rollback capability

Technical Implementation:

• IaC tools: Terraform, Pulumi, CloudFormation, Azure ARM templates
• Configuration: Helm, Kustomize, Jsonnet for Kubernetes
• GitOps: ArgoCD or FluxCD for Kubernetes deployment
• State management: Terraform Cloud, S3 backend with state locking

---

Backup & Disaster Recovery

Purpose: Ensures data durability and business continuity through backups and DR planning.

Key Responsibilities:

• Backup strategies
  • Automated database backups (daily full, hourly incremental)
  • Application state backups (Kubernetes resources, ConfigMaps)
  • Object storage replication (cross-region)
  • Configuration backups (IaC state, secrets)
• Recovery procedures
  • Point-in-time database recovery
  • Application redeployment from GitOps
  • Secrets restoration from backup
  • Data consistency validation
• Disaster recovery
  • Multi-region deployment for failover
  • DR runbooks and testing
  • RTO/RPO targets (Recovery Time/Point Objectives)
  • Regular DR drills (quarterly failover tests)
• Data retention
  • Backup retention policies (30 days, 1 year, 7 years)
  • Compliance-driven retention
  • Automated backup cleanup
  • Backup encryption and security

Technical Implementation:

• Database backups: Automated snapshots, WAL archiving, cross-region replication
• Kubernetes backups: Velero for cluster backup and restore
• Object storage: S3 versioning, cross-region replication
• Testing: Automated backup validation, regular restore tests

---

5.8 Infrastructure Deployment & Operations

High Availability:

• Multi-AZ deployment for all critical components
• Multi-region for global reach and disaster recovery
• No single points of failure
• Automated failover for databases, load balancers
• Health checks and self-healing (Kubernetes restarts unhealthy pods)

Scalability:

• Horizontal auto-scaling for compute (Gateway, Portal, workers)
• Database read replicas for query scaling
• CDN for static content distribution
• Caching layers to reduce backend load
• Partitioning and sharding for large datasets

Cost Optimization:

• Right-sizing resources based on actual usage
• Auto-scaling to avoid over-provisioning
• Reserved instances / Savings Plans for predictable workloads
• Spot instances for batch processing
• Storage tiering (hot/warm/cold)
• Resource tagging for cost attribution

Security:

• Network isolation (VPCs, private subnets)
• Least-privilege IAM policies
• Encryption at rest and in transit
• Security scanning (SAST, DAST, container scanning)
• Regular patching and updates
• Compliance frameworks (SOC 2, PCI-DSS, HIPAA)

Operational Excellence:

• Infrastructure as Code for all resources
• GitOps for declarative deployments
• Automated testing and validation
• Comprehensive monitoring and alerting
• Runbooks and documentation
• Regular chaos engineering exercises

---

Next: Integration & Automation

Back to Overview