Data Integration
ETL vs. ELT, CDC with Debezium, data pipeline orchestration with Airflow, and real-time streaming.
Data Integration is a core discipline within Data Architecture. It defines how technology systems should be designed, implemented, and governed to achieve reliable, secure, and maintainable outcomes that serve both technical teams and business stakeholders.
Applying Data Integration standards reduces system failures, accelerates delivery, and provides the governance evidence required by enterprise clients, regulators like BSP, and certification bodies like ISO. Top technology companies (Google, Microsoft, Amazon) treat these standards as competitive differentiators, not compliance overhead.
📖 Detailed Explanation
Data architecture encompasses the models, flows, stores, governance, and pipelines that manage an organization's data assets. From operational databases to analytics platforms, data architecture directly affects decision-making quality and regulatory compliance.
Industry Context: Modern data platforms: dbt, Apache Spark, Snowflake, Databricks, Apache Kafka for streaming.
Relevance to Philippine Financial Services: Organizations operating under BSP supervision must demonstrate mature data architecture practices during technology examinations. The BSP Technology Supervision Group evaluates documentation quality, process maturity, and evidence of systematic practice — all of which are addressed by the standards in this section.
Alignment to Global Standards: The practices documented here are aligned to frameworks used by Google, Amazon, Microsoft, and the world's leading consulting firms (McKinsey Digital, Deloitte Technology, Accenture Technology). They represent the current industry consensus on best practices rather than any single vendor's approach.
Engineering Perspective: For engineers, Data Integration provides concrete patterns and anti-patterns that prevent common mistakes and accelerate development by providing proven solutions to recurring problems. Rather than rediscovering what doesn't work, teams can apply battle-tested approaches with known trade-offs.
Architecture Perspective: For architects, Data Integration provides the design vocabulary, decision frameworks, and governance artifacts needed to make and communicate complex technical decisions clearly and consistently.
Business Perspective: For business stakeholders, Data Integration provides assurance that technology investments are aligned to industry standards, reducing the risk of expensive rework, regulatory findings, and system failures that impact customers and revenue.
📈 Architecture Diagram
flowchart LR
A["Data Integration
Concept"] --> B["Principles
& Standards"]
B --> C["Design
Decisions"]
C --> D["Implementation
Patterns"]
D --> E["Governance
Checkpoints"]
E --> F["Validation
& Evidence"]
F -.->|"Feedback Loop"| A
style A fill:#1e293b,color:#f8fafc
style F fill:#052e16,color:#4ade80
Lifecycle of Data Integration: from concept through principles, design decisions, implementation patterns, governance checkpoints, and validation — with feedback loops for continuous improvement.
🌎 Real-World Examples
Spotify was an early Data Mesh adopter. Each domain team (Playlists, Recommendations, Artist Analytics) owns their data products — they define schemas, SLAs, and access policies. Their 'Backstage' developer portal (open-sourced) serves as the data catalog where teams register and discover data products. Cross-domain data access goes through well-defined data product interfaces, never direct database queries.
✓ Result: Data product discovery time reduced from days to minutes; data quality incidents dropped 60% after domain ownership was established
Snowflake's own internal data architecture is the reference implementation of their platform's capabilities: a single Data Cloud with data sharing across 7,000+ customers via Snowflake Secure Data Sharing. Their engineering team uses Snowflake to monitor Snowflake — internal metrics, usage data, and query performance all flow through the same platform they sell. Zero ETL data movement between departments.
✓ Result: Single data platform for 7,000+ enterprise customers; cross-organization data sharing with zero data movement latency
LinkedIn created Apache Kafka (open-sourced in 2011) to solve their data pipeline problem: 175+ applications producing data that 200+ applications need to consume. Their current platform processes 7 trillion+ events per day. They invented the Lambda Architecture (batch + speed layers) and later helped drive the Kappa Architecture (stream-only). Their engineering blog is one of the most influential data engineering resources.
✓ Result: 7 trillion events/day processed with < 5 second end-to-end latency; Kafka now used by 80% of Fortune 100 companies
Wise's data architecture for cross-border payments enforces immutability at every layer: every payment event is append-only (event sourcing), every balance change has an immutable audit trail, and data reconciliation runs continuously to detect discrepancies. Their 'double-entry bookkeeping' pattern applied at the database level ensures financial data integrity that satisfies FCA, FinCEN, and MAS regulatory requirements simultaneously.
✓ Result: Zero financial reconciliation failures in 8 years of operation; $12B+ monthly payment volume with 100% audit trail completeness
🌟 Core Principles
Every aspect of data integration must be deliberately designed, not discovered after deployment. Document design decisions as ADRs with explicit rationale.
Apply data integration practices consistently across all systems. Inconsistent application creates governance blind spots and makes incident investigation unpredictable.
Data Integration practices must demonstrably contribute to business outcomes: reduced downtime, faster delivery, lower operational cost, or improved compliance posture.
Quality of data integration implementation must be measurable. Define specific metrics and collect evidence continuously — not only at audit or review time.
Standards for data integration evolve as technology and threat landscapes change. Schedule quarterly reviews of applicable standards and update practices accordingly.
⚙️ Implementation Steps
Current State Assessment
Document the current state of data integration practice: what is implemented, what is missing, what is inconsistent across teams. Use the governance/scorecards section for a structured assessment framework.
Gap Analysis Against Standards
Compare current state against the standards in this section and applicable frameworks (DAMA DMBOK — Data Management Body of Knowledge, Data Mesh Principles — Zhamak Dehghani). Prioritize gaps by business impact and remediation effort.
Design the Target State
Define the target data integration state: which patterns will be adopted, which anti-patterns eliminated, which governance mechanisms introduced. Express as a time-bound roadmap.
Incremental Implementation
Implement data integration improvements incrementally: pilot with one team or system, measure outcomes, refine the approach, then expand. Avoid big-bang transformations.
Validate and Iterate
Measure the impact of implemented changes against defined success criteria. Incorporate lessons learned into the practice standards. Contribute improvements back to this library.
✅ Governance Checkpoints
| Checkpoint | Owner | Gate Criteria | Status |
|---|---|---|---|
| Current State Documented | Solution Architect | Data Integration current state assessment completed and reviewed | Required |
| Gap Analysis Reviewed | Architecture Review Board | Gap analysis reviewed and prioritization approved | Required |
| Implementation Plan Approved | Enterprise Architect | Target state and roadmap approved by ARB | Required |
| Quality Metrics Defined | Solution Architect | Measurable success criteria defined for data integration improvements | Required |
◈ Recommended Patterns
✦ Reference Architecture Adoption
Start from an established reference architecture for data integration rather than designing from scratch. Adapt to organizational context rather than rebuilding proven foundations.
✦ Pattern Library Contribution
When your team solves a recurring data integration problem with a novel approach, document it as a pattern for the library. This compounds organizational knowledge over time.
✦ Fitness Function Testing
Encode data integration standards as automated architectural fitness functions — tests that run in CI/CD and fail builds when standards are violated. This makes governance continuous rather than periodic.
⛔ Anti-Patterns to Avoid
⛔ Standards Theater
Documenting data integration standards in architecture policies that no one reads and no one enforces. Standards without automated validation or governance gates are not operational standards.
⛔ Copy-Paste Architecture
Adopting another organization's data integration patterns wholesale without adapting to organizational context, team capability, or regulatory environment. Always adapt; never just copy.
🤖 AI Augmentation Extensions
LLM agents analyze design documents against data integration standards, generating structured gap reports with cited evidence and suggested remediation approaches.
This section is optimized for vector ingestion into an AI-powered architecture assistant. Semantic search enables architects to retrieve relevant data integration guidance through natural language queries.
🔗 Related Sections
📚 References & Further Reading
- DAMA DMBOK — Data Management Body of Knowledge↗ IEEE Xplore
- Data Mesh Principles — Zhamak Dehghani↗ IEEE Xplore
- Kimball Dimensional Modeling↗ IEEE Xplore
- Delta Architecture↗ IEEE Xplore
- Documenting Software Architectures — Bass, Clements, Kazman↗ Amazon
- Building Evolutionary Architectures — Ford, Parsons, Kua↗ O'Reilly