This architecture follows established patterns for modern data platforms. High confidence in the overall approach; specific tool selection and cost projections require validation against your environment.
Human Validation Recommended
- •Validate cost estimates against actual AWS pricing
- •Confirm network connectivity to all data sources
- •Review data classification with legal/compliance
Deployment Intent
This architecture enables business analysts to access governed data without waiting on engineering, while maintaining strict controls around sensitive data and predictable cloud spend.
Primary Objective
Enable self-service analytics while maintaining data governance and cost control
Outcome Owner
Chief Data Officer
Optimize For
- ↑Query performance for analysts
- ↑Data freshness (near real-time)
- ↑Cost predictability
Cannot Break
- ✕PII data isolation
- ✕Audit logging for compliance
- ✕Existing BI dashboard integrations
Can Degrade
- ~Cross-region replication latency
- ~Historical data access speed (>2 years)
- ~Ad-hoc export throughput
Environmental Constraints
Mid-sized financial services firm (500 employees) with existing on-premises data warehouse, growing cloud footprint on AWS, and regulatory requirements including SOX and state privacy laws. Current pain points include 2-week lead time for new data requests and inconsistent metric definitions across departments.
Reference Architecture
SIEM Architecture
Security monitoring for financial services on AWS
Logical architecture for your specific context. Actual implementation will vary.
Control Requirements
Required controls across identity, data, runtime, network, and governance layers.
Identity Controls
- •SSO integration with corporate IdP (Azure AD)
- •Role-based access tied to data classification levels
- •Service account management with rotation
- •Break-glass procedures for emergency access
Data Controls
- •Column-level encryption for PII fields
- •Dynamic data masking based on user role
- •Data classification tagging at ingestion
- •Retention policies enforced automatically
Runtime Controls
- •Query cost limits per user/group
- •Workload isolation between production and development
- •Resource quotas to prevent runaway queries
- •Scheduled scaling for predictable workloads
Network Controls
- •Private connectivity to data sources
- •VPC peering for internal consumers
- •IP allowlisting for external BI tools
- •Encryption in transit (TLS 1.3)
Governance Controls
- •Automated data quality checks with alerting
- •Centralized metric definitions (semantic layer)
- •Change management for transformation logic
- •Data lineage captured end-to-end
Operating Model
Clear separation between platform (infrastructure) and product (data models) ownership. Self-service for consumers, guardrails for governance.
Ownership Boundaries
| Area | Owner |
|---|---|
| Infrastructure & Platform | Platform Engineering |
| Data Ingestion Pipelines | Data Engineering |
| Transformation Logic | Analytics Engineering |
| Semantic Layer Definitions | Business Intelligence |
| Data Classification | Data Governance |
| Access Requests | Data Governance + IT Security |
Required Capabilities
- •Infrastructure as Code (Terraform/Pulumi)
- •dbt for transformation orchestration
- •Data catalog administration
- •Cost monitoring and optimization
- •Incident response for data quality issues
- •Self-service onboarding workflows
Day-2 Burden
Medium - Automated pipelines reduce manual work, but semantic layer maintenance and access management require ongoing attention. Expect 0.5 FTE dedicated to platform operations.
Failure Handling
Pipeline failures trigger alerts to data engineering. SLA: 4-hour response for critical pipelines, 24-hour for non-critical. Consumers see stale data warnings rather than broken dashboards.
Change Velocity
Weekly releases for transformation logic, monthly for infrastructure changes. Emergency changes require data governance approval.
Tradeoffs & Boundaries
What this architecture optimizes for—and what it sacrifices.
Lakehouse over Traditional Warehouse
Choosing a lakehouse architecture (object storage + query engine) over a traditional data warehouse provides flexibility and cost advantages but requires more operational maturity.
Consideration: Team must be comfortable with eventual consistency and managing storage tiers.
Semantic Layer Centralization
Centralizing metric definitions in a semantic layer ensures consistency but creates a bottleneck for new metric requests.
Consideration: Invest in self-service tooling and clear SLAs for metric definition requests.
Cost Control vs. Performance
Implementing query cost limits protects budget but may frustrate power users running complex analyses.
Consideration: Create tiered access levels with different cost ceilings for different user personas.
Role-Based Perspectives
Same architecture, different lenses. Select the perspective most relevant to your role.
CIO / IT Leadership
Total cost of ownership and organizational readiness
CIO / IT Leadership
Total cost of ownership and organizational readiness
- •Platform consolidation opportunity - replace 3+ point solutions
- •Skills gap: need 2-3 engineers with modern data stack experience
- •12-18 month payback period based on reduced analyst wait time
- •Vendor selection should prioritize managed services to reduce ops burden
CISO / Security
Data protection and compliance posture
CISO / Security
Data protection and compliance posture
- •PII handling requires encryption at rest and in transit
- •Access logging must integrate with existing SIEM
- •Third-party BI tool access needs security review
- •Incident response playbooks needed for data breach scenarios
Enterprise Architect
Integration patterns and technical standards
Enterprise Architect
Integration patterns and technical standards
- •Event-driven ingestion preferred over batch where possible
- •Schema registry required for data contract enforcement
- •Metadata standards must align with existing data catalog
- •Consider multi-cloud portability for strategic flexibility
Chief Data Officer / Data Leader
Data quality, governance, and business value
Chief Data Officer / Data Leader
Data quality, governance, and business value
- •Data quality metrics should be exposed to business stakeholders
- •Semantic layer enables single source of truth for metrics
- •Self-service reduces time-to-insight from weeks to hours
- •Data literacy program needed for successful adoption
Explicit Assumptions
This output is based on the following assumptions. Validate these against your actual environment.
Organizational
- •Executive sponsorship for data governance exists
- •Analytics engineering function is established or planned
- •Budget approved for cloud data platform investment
Technical
- •Primary cloud provider is AWS (adjustable to Azure/GCP)
- •Existing data sources have APIs or change data capture capability
- •Network connectivity to on-premises systems is available
Regulatory
- •Data residency requirements allow cloud storage
- •Audit log retention of 7 years is sufficient
- •No real-time regulatory reporting requirements
What This Does NOT Decide
The following items are explicitly out of scope for this reference architecture:
Disclaimer
This reference architecture is generated based on the information provided and represents general best practices. It does not constitute professional advice. Validate all recommendations against your specific regulatory, technical, and organizational requirements before implementation. No vendor endorsement is implied.
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